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Timezone: America/New_York
Full Schedule Sun 7/17 Mon 7/18 Tue 7/19 Wed 7/20 Thu 7/21 Fri 7/22 Sat 7/23
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Invited Talk
9:00 AM - 10:00 AM

Abstract coming soon...

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Speaker Bio
Aviv Regev
Aviv Regev is a computational biologist and systems biologist and Executive Vice President and Head of Genentech Research and Early Development in Genentech/Roche. She is a core member at the Broad Institute of MIT and Harvard and professor at the Department of Biology of the Massachusetts Institute of Technology. In 2020, Regev became the Head and Executive Vice President of Genentech Research and Early Development, based in South San Francisco, and a member of the extended Corporate Executive Committee of Roche. Previously, she was a Core Institute Member (now on leave), Chair of the Faculty, Founding Director of the Klarman Cell Observatory and co-Director Cell Circuits Program at the Broad Institute of MIT and Harvard. She was also a professor in the Department of Biology at the Massachusetts Institute of Technology (now on leave), as well as an Investigator at the Howard Hughes Medical Institute. Regev's research includes work on gene expression (with Eran Segal and David Botstein), and the use of π-calculus to represent biochemical processes. Regev’s team has been a leading pioneer of single-cell genomics experimental and computational methods.
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Spotlight
10:30 AM - 12:00 PM
14 Events in this session
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
Namuk Park · Songkuk Kim
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Julian Bitterwolf · Alexander Meinke · Maximilian Augustin · Matthias Hein
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
Hidetaka Kamigaito · Katsuhiko Hayashi
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Tianlong Chen · Huan Zhang · Zhenyu Zhang · Shiyu Chang · Sijia Liu · Pin-Yu Chen · Zhangyang “Atlas” Wang
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
Lu Bai · Lixin Cui · Edwin Hancock
Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time
David Woodruff · Amir Zandieh
Random Gegenbauer Features for Scalable Kernel Methods
Insu Han · Amir Zandieh · Haim Avron
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Dong Chen · Lingfei Wu · Siliang Tang · Xiao Yun · Bo Long · Yueting Zhuang
Functional Output Regression with Infimal Convolution: Exploring the Huber and $\epsilon$-insensitive Losses
Alex Lambert · Dimitri Bouche · Zoltan Szabo · Florence d'Alché-Buc
Measuring dissimilarity with diffeomorphism invariance
Théophile Cantelobre · Carlo Ciliberto · Benjamin Guedj · Alessandro Rudi
Importance Weighted Kernel Bayes' Rule
Liyuan Xu · Yutian Chen · Arnaud Doucet · Arthur Gretton
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
Meyer Scetbon · Laurent Meunier · Yaniv Romano
Nyström Kernel Mean Embeddings
Antoine Chatalic · Nicolas Schreuder · Lorenzo Rosasco · Alessandro Rudi
Distribution Regression with Sliced Wasserstein Kernels
Dimitri Marie Meunier · Massimiliano Pontil · Carlo Ciliberto
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Oral
10:30 AM - 12:00 PM
12 Events in this session
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
sajad khodadadian · PRANAY SHARMA · Gauri Joshi · Siva Maguluri
Entropic Gromov-Wasserstein between Gaussian Distributions
Khang Le · Dung Le · Huy Nguyen · · Tung Pham · Nhat Ho
No-Regret Learning in Partially-Informed Auctions
Wenshuo Guo · Michael Jordan · Ellen Vitercik
On Last-Iterate Convergence Beyond Zero-Sum Games
Ioannis Anagnostides · Ioannis Panageas · Gabriele Farina · Tuomas Sandholm
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
Gabriele Farina · Chung-Wei Lee · Haipeng Luo · Christian Kroer
Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games
Lucas Baudin · Rida Laraki
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming
Gregory Dexter · Agniva Chowdhury · Haim Avron · Petros Drineas
Nested Bandits
Matthieu Martin · Panayotis Mertikopoulos · Thibaud J Rahier · Houssam Zenati
Information Discrepancy in Strategic Learning
Yahav Bechavod · Chara Podimata · Steven Wu · Juba Ziani
A Psychological Theory of Explainability
Scott Cheng-Hsin Yang · Nils Erik Tomas Folke · Patrick Shafto
Task-aware Privacy Preservation for Multi-dimensional Data
Jiangnan Cheng · Ao Tang · Sandeep Chinchali
Strategic Representation
Vineet Nair · Ganesh Ghalme · Inbal Talgam-Cohen · Nir Rosenfeld
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Spotlight
10:30 AM - 12:00 PM
15 Events in this session
Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation
Steffen Schotthöfer · Tianbai Xiao · Martin Frank · Cory Hauck
Composing Partial Differential Equations with Physics-Aware Neural Networks
Matthias Karlbauer · Timothy Praditia · Sebastian Otte · Sergey Oladyshkin · Wolfgang Nowak · Martin V Butz
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Uri Alon · Frank Xu · Junxian He · Sudipta Sengupta · Dan Roth · Graham Neubig
Towards Coherent and Consistent Use of Entities in Narrative Generation
Pinelopi Papalampidi · Kris Cao · Tomas Kocisky
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
Shiran Zada · Itay Benou · Michal Irani
Optimally Controllable Perceptual Lossy Compression
Zeyu Yan · Fei Wen · Peilin Liu
Learning to Solve PDE-constrained Inverse Problems with Graph Networks
QINGQING ZHAO · David B. Lindell · Gordon Wetzstein
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
Yupu Lu · Shijie Lin · Guanqi Chen · Jia Pan
Learning to Estimate and Refine Fluid Motion with Physical Dynamics
Mingrui Zhang · Jianhong Wang · James Tlhomole · Matthew Piggott
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
Manuel Brenner · Florian Hess · Jonas M Mikhaeil · Leonard Bereska · Zahra Monfared · Po-Chen Kuo · Daniel Durstewitz
An Intriguing Property of Geophysics Inversion
Yinan Feng · Yinpeng Chen · Shihang Feng · Peng Jin · Zicheng Liu · Youzuo Lin
Particle Transformer for Jet Tagging
Huilin Qu · Congqiao Li · Sitian Qian
BabelTower: Learning to Auto-parallelized Program Translation
Yuanbo Wen · Qi Guo · Qiang Fu · XiaQing Li · jianxing xu · Yanlin Tang · Yongwei Zhao · Xing Hu · Zidong Du · Ling Li · Chao Wang · Xuehai Zhou · Yunji Chen
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
Kaizhi Qian · Yang Zhang · Heting Gao · Junrui Ni · Cheng-I Lai · David Cox · Mark Hasegawa-Johnson · Shiyu Chang
On Distribution Shift in Learning-based Bug Detectors
Jingxuan He · Luca Beurer-Kellner · Martin Vechev
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Spotlight
10:30 AM - 12:00 PM
14 Events in this session
Does the Data Induce Capacity Control in Deep Learning?
Rubing Yang · Jialin Mao · Pratik Chaudhari
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Chuan Wen · Jianing Qian · Jierui Lin · Jiaye Teng · Dinesh Jayaraman · Yang Gao
Memory-Based Model Editing at Scale
Eric Mitchell · Charles Lin · Antoine Bosselut · Christopher Manning · Chelsea Finn
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
John Rachwan · Daniel Zügner · Bertrand Charpentier · Simon Geisler · Morgane Ayle · Stephan Günnemann
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Guy Hacohen · Avihu Dekel · Daphna Weinshall
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
Byunggook Na · Jisoo Mok · Seongsik Park · Dongjin Lee · Hyeokjun Choe · Sungroh Yoon
Overcoming Oscillations in Quantization-Aware Training
Markus Nagel · Marios Fournarakis · Yelysei Bondarenko · Tijmen Blankevoort
Dataset Condensation via Efficient Synthetic-Data Parameterization
Jang-Hyun Kim · Jinuk Kim · Seong Joon Oh · Sangdoo Yun · Hwanjun Song · Joonhyun Jeong · Jung-Woo Ha · Hyun Oh Song
Searching for BurgerFormer with Micro-Meso-Macro Space Design
Longxing Yang · Yu Hu · Shun Lu · Zihao Sun · Jilin Mei · Yinhe Han · Xiaowei Li
Multi-scale Feature Learning Dynamics: Insights for Double Descent
Mohammad Pezeshki · Amartya Mitra · Yoshua Bengio · Guillaume Lajoie
Dataset Condensation with Contrastive Signals
Saehyung Lee · SANGHYUK CHUN · Sangwon Jung · Sangdoo Yun · Sungroh Yoon
Equivariant Priors for compressed sensing with unknown orientation
Anna Kuzina · Kumar Pratik · Fabio Valerio Massoli · Arash Behboodi
Injecting Logical Constraints into Neural Networks via Straight-Through Estimators
Zhun Yang · Joohyung Lee · Chiyoun Park
Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt
Sören Mindermann · Jan Brauner · Muhammed Razzak · Mrinank Sharma · Andreas Kirsch · Winnie Xu · Benedikt Höltgen · Aidan Gomez · Adrien Morisot · Sebastian Farquhar · Yarin Gal
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Oral
10:30 AM - 12:00 PM
12 Events in this session
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Andrew Wagenmaker · Yifang Chen · Max Simchowitz · Simon Du · Kevin Jamieson
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more
Elad Tolochinksy · Ibrahim Jubran · Dan Feldman
Shuffle Private Linear Contextual Bandits
Sayak Ray Chowdhury · Xingyu Zhou
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Laixi Shi · Gen Li · Yuting Wei · Yuxin Chen · Yuejie Chi
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Andrew Wagenmaker · Yifang Chen · Max Simchowitz · Simon Du · Kevin Jamieson
Label Ranking through Nonparametric Regression
Dimitris Fotakis · Alkis Kalavasis · Eleni Psaroudaki
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
Dan Qiao · Ming Yin · Ming Min · Yu-Xiang Wang
A Simple Unified Framework for High Dimensional Bandit Problems
Wenjie Li · Adarsh Barik · Jean Honorio
A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds
Jiahao He · Jiheng Zhang · Rachel Q. Zhang
Branching Reinforcement Learning
Yihan Du · Wei Chen
Fast rates for noisy interpolation require rethinking the effect of inductive bias
Konstantin Donhauser · Nicolò Ruggeri · Stefan Stojanovic · Fanny Yang
Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path
Haoyuan Cai · Tengyu Ma · Simon Du
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Spotlight
10:30 AM - 12:00 PM
15 Events in this session
A Neural Tangent Kernel Perspective of GANs
Jean-Yves Franceschi · Emmanuel de Bézenac · Ibrahim Ayed · Mickael Chen · Sylvain Lamprier · Patrick Gallinari
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
Jen-Hao Rick Chang · Ashish Shrivastava · Hema Koppula · Xiaoshuai Zhang · Oncel Tuzel
Neural Inverse Transform Sampler
Henry Li · Yuval Kluger
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement
Wengong Jin · Regina Barzilay · Tommi Jaakkola
Diffusion Models for Adversarial Purification
Weili Nie · Brandon Guo · Yujia Huang · Chaowei Xiao · Arash Vahdat · Animashree Anandkumar
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Junwen Bai · Shufeng Kong · Carla Gomes
VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis
Tathagat Verma · Abir De · Yateesh Agrawal · Vishwa Vinay · Soumen Chakrabarti
It’s Raw! Audio Generation with State-Space Models
Karan Goel · Albert Gu · Chris Donahue · Christopher Re
Unsupervised Image Representation Learning with Deep Latent Particles
Tal Daniel · Aviv Tamar
Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks
Weiming Zhi · Tin Lai · Lionel Ott · Edwin V Bonilla · Fabio Ramos
Neuro-Symbolic Hierarchical Rule Induction
Claire Glanois · Zhaohui Jiang · Xuening Feng · Paul Weng · Matthieu Zimmer · Dong Li · Wulong Liu · Jianye Hao
General-purpose, long-context autoregressive modeling with Perceiver AR
Curtis Hawthorne · Andrew Jaegle · Cătălina Cangea · Sebastian Borgeaud · Charlie Nash · Mateusz Malinowski · Sander Dieleman · Oriol Vinyals · Matthew Botvinick · Ian Simon · Hannah Sheahan · Neil Zeghidour · Jean-Baptiste Alayrac · Joao Carreira · Jesse Engel
Marginal Tail-Adaptive Normalizing Flows
Mike Laszkiewicz · Johannes Lederer · Asja Fischer
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
Xiang Xu · Karl Willis · Joseph G Lambourne · Chin-Yi Cheng · Pradeep Kumar Jayaraman · Yasutaka Furukawa
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
Shanyan Guan · Huayu Deng · Yunbo Wang · Xiaokang Yang
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Oral
10:30 AM - 12:00 PM
12 Events in this session
The Importance of Non-Markovianity in Maximum State Entropy Exploration
Mirco Mutti · Riccardo De Santi · Marcello Restelli
Continuous Control with Action Quantization from Demonstrations
Robert Dadashi · Léonard Hussenot · Damien Vincent · Sertan Girgin · Anton Raichuk · Matthieu Geist · Olivier Pietquin
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization
Minghuan Liu · Zhengbang Zhu · Yuzheng Zhuang · Weinan Zhang · Jianye Hao · Yong Yu · Jun Wang
Inverse Contextual Bandits: Learning How Behavior Evolves over Time
Alihan Hüyük · Daniel Jarrett · Mihaela van der Schaar
Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning
Angelo Damiani · Giorgio Manganini · Alberto Maria Metelli · Marcello Restelli
Towards Uniformly Superhuman Autonomy via Subdominance Minimization
Brian Ziebart · Sanjiban Choudhury · Xinyan Yan · Paul Vernaza
Causal Imitation Learning under Temporally Correlated Noise
Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu
Interactive Inverse Reinforcement Learning for Cooperative Games
Thomas Kleine Buening · Anne-Marie George · Christos Dimitrakakis
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
Weichao Zhou · Wenchao Li
Robust Imitation Learning against Variations in Environment Dynamics
Jongseong Chae · Seungyul Han · Whiyoung Jung · MYUNG-SIK CHO · Sungho Choi · Youngchul Sung
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
Haoran Xu · Xianyuan Zhan · Honglei Yin · Huiling qin
Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation
ZHIHAN LIU · Yufeng Zhang · Zuyue Fu · Zhuoran Yang · Zhaoran Wang
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Spotlight
10:30 AM - 12:00 PM
13 Events in this session
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
Shuo Yang · Erkun Yang · Bo Han · Yang Liu · Min Xu · Gang Niu · Tongliang Liu
Invariant Ancestry Search
Phillip Bredahl Mogensen · Nikolaj Thams · Jonas Peters
Unaligned Supervision for Automatic Music Transcription in The Wild
Ben Maman · Amit Bermano
Fourier Learning with Cyclical Data
Yingxiang Yang · Zhihan Xiong · Tianyi Liu · Taiqing Wang · Chong Wang
Linear Adversarial Concept Erasure
Shaul Ravfogel · Michael Twiton · Yoav Goldberg · Ryan Cotterell
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Paul Rolland · Volkan Cevher · Matthäus Kleindessner · Chris Russell · Dominik Janzing · Bernhard Schölkopf · Francesco Locatello
Provable Domain Generalization via Invariant-Feature Subspace Recovery
Haoxiang Wang · Haozhe Si · Bo Li · Han Zhao
Subspace Learning for Effective Meta-Learning
Weisen JIANG · James Kwok · Yu Zhang
Continual Learning via Sequential Function-Space Variational Inference
Tim G. J Rudner · Freddie Bickford Smith · QIXUAN FENG · Yee-Whye Teh · Yarin Gal
Efficient Test-Time Model Adaptation without Forgetting
Shuaicheng Niu · Jiaxiang Wu · Yifan Zhang · Yaofo Chen · Shijian Zheng · Peilin Zhao · Mingkui Tan
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Alexandre Capone · Armin Lederer · Sandra Hirche
Input Dependent Sparse Gaussian Processes
Bahram Jafrasteh · Carlos Villacampa-Calvo · Daniel Hernandez-Lobato
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Da Long · Zheng Wang · Aditi Krishnapriyan · Robert Kirby · Shandian Zhe · Michael Mahoney
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Spotlight
10:30 AM - 12:00 PM
15 Events in this session
Adapting k-means Algorithms for Outliers
Christoph Grunau · Vaclav Rozhon
Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization
Karan Chadha · Gary Cheng · John Duchi
Online Algorithms with Multiple Predictions
Keerti Anand · Rong Ge · Amit Kumar · Debmalya Panigrahi
Parsimonious Learning-Augmented Caching
Sungjin Im · Ravi Kumar · Aditya Petety · Manish Purohit
RUMs from Head-to-Head Contests
Matteo Almanza · Flavio Chierichetti · Ravi Kumar · Alessandro Panconesi · Andrew Tomkins
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Rahul Mazumder · Xiang Meng · Haoyue Wang
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach
Cedric Malherbe · Kevin Scaman
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
Simone Parisi · Aravind Rajeswaran · Senthil Purushwalkam · Abhinav Gupta
COLA: Consistent Learning with Opponent-Learning Awareness
Timon Willi · Alistair Letcher · Johannes Treutlein · Jakob Foerster
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
Wei Xiong · Han Zhong · Chengshuai Shi · Cong Shen · Tong Zhang
A Framework for Learning to Request Rich and Contextually Useful Information from Humans
Khanh Nguyen · Yonatan Bisk · Hal Daumé III
Learning Stochastic Shortest Path with Linear Function Approximation
Yifei Min · Jiafan He · Tianhao Wang · Quanquan Gu
Difference Advantage Estimation for Multi-Agent Policy Gradients
yueheng li · Guangming Xie · Zongqing Lu
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Ling Pan · Longbo Huang · Tengyu Ma · Huazhe Xu
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
Han Zhong · Wei Xiong · Jiyuan Tan · Liwei Wang · Tong Zhang · Zhaoran Wang · Zhuoran Yang
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Spotlight
10:30 AM - 12:00 PM
15 Events in this session
Equivariance versus Augmentation for Spherical Images
Jan Gerken · Oscar Carlsson · Hampus Linander · Fredrik Ohlsson · Christoffer Petersson · Daniel Persson
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Charbel Sakr · Steve Dai · Rangha Venkatesan · Brian Zimmer · William Dally · Brucek Khailany
Neural Network Poisson Models for Behavioural and Neural Spike Train Data
Moein Khajehnejad · Forough Habibollahi · Richard Nock · Ehsan Arabzadeh · Peter Dayan · Amir Dezfouli
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
Huan Zhang · Shiqi Wang · Kaidi Xu · Yihan Wang · Suman Jana · Cho-Jui Hsieh · Zico Kolter
GACT: Activation Compressed Training for Generic Network Architectures
Xiaoxuan Liu · Lianmin Zheng · Dequan Wang · Yukuo Cen · Weize Chen · Xu Han · Jianfei Chen · Zhiyuan Liu · Jie Tang · Joseph Gonzalez · Michael Mahoney · Alvin Cheung
Fast Finite Width Neural Tangent Kernel
Roman Novak · Jascha Sohl-Dickstein · Samuel Schoenholz
G-Mixup: Graph Data Augmentation for Graph Classification
Xiaotian Han · Zhimeng Jiang · Ninghao Liu · Xia Hu
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
Beren Millidge · Tommaso Salvatori · Yuhang Song · Thomas Lukasiewicz · Rafal Bogacz
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
Yan Zeng · Xinsong Zhang · Hang Li
Continual Learning with Guarantees via Weight Interval Constraints
Maciej Wołczyk · Karol J. Piczak · Bartosz Wójcik · Łukasz Pustelnik · Paweł Morawiecki · Jacek Tabor · Tomasz Trzcinski · Przemysław Spurek
Faster Fundamental Graph Algorithms via Learned Predictions
Justin Chen · Sandeep Silwal · Ali Vakilian · Fred Zhang
Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering
Lorenzo Orecchia · Konstantinos Ameranis · Charalampos Tsourakakis · Kunal Talwar
Fair and Fast k-Center Clustering for Data Summarization
Haris Angelidakis · Adam Kurpisz · Leon Sering · Rico Zenklusen
Online and Consistent Correlation Clustering
Vincent Cohen-Addad · Silvio Lattanzi · Andreas Maggiori · Nikos Parotsidis
Generalized Leverage Scores: Geometric Interpretation and Applications
Bruno Ordozgoiti · Antonis Matakos · Aristides Gionis
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Spotlight
1:30 PM - 3:00 PM
14 Events in this session
Dynamic Topic Models for Temporal Document Networks
Delvin Zhang · Hady Lauw
A Functional Information Perspective on Model Interpretation
Itai Gat · Nitay Calderon · Roi Reichart · Tamir Hazan
Be Like Water: Adaptive Floating Point for Machine Learning
Thomas Y. Yeh · Maxwell Sterner · Zerlina Lai · Brandon Chuang · Alexander Ihler
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Johannes Brandstetter · Max Welling · Daniel Worrall
Fast Provably Robust Decision Trees and Boosting
Jun-Qi Guo · Ming-Zhuo Teng · Wei Gao · Zhi-Hua Zhou
Order Constraints in Optimal Transport
Yu Chin Fabian Lim · Laura Wynter · Shiau Hong Lim
Sublinear-Time Clustering Oracle for Signed Graphs
Stefan Neumann · Pan Peng
PAC-Bayesian Bounds on Rate-Efficient Classifiers
Alhabib Abbas · Yiannis Andreopoulos
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees
Osman Asif Malik
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Momin Abbas · Quan Xiao · Lisha Chen · Pin-Yu Chen · Tianyi Chen
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum
Hongchang Gao · Junyi Li · Heng Huang
SPDY: Accurate Pruning with Speedup Guarantees
Elias Frantar · Dan Alistarh
Flashlight: Enabling Innovation in Tools for Machine Learning
Jacob Kahn · Vineel Pratap · Tatiana Likhomanenko · Qiantong Xu · Awni Hannun · Jeff Cai · Paden Tomasello · Ann Lee · Edouard Grave · Gilad Avidov · Benoit Steiner · Vitaliy Liptchinsky · Gabriel Synnaeve · Ronan Collobert
On the Robustness of CountSketch to Adaptive Inputs
Edith Cohen · Xin Lyu · Jelani Nelson · Tamas Sarlos · Moshe Shechner · Uri Stemmer
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Oral
1:30 PM - 3:00 PM
12 Events in this session
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
Konstantinos Kalais · Sotirios Chatzis
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
Zheng Wang · Shandian Zhe
Nonparametric Embeddings of Sparse High-Order Interaction Events
Zheng Wang · Yiming Xu · Conor Tillinghast · Shibo Li · Akil Narayan · Shandian Zhe
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antorán · David Janz · James Allingham · Erik Daxberger · Riccardo Barbano · Eric Nalisnick · Jose Miguel Hernandez-Lobato
NOMU: Neural Optimization-based Model Uncertainty
Jakob Heiss · Jakob Weissteiner · Hanna Wutte · Sven Seuken · Josef Teichmann
Bayesian Model Selection, the Marginal Likelihood, and Generalization
Sanae Lotfi · Pavel Izmailov · Gregory Benton · Micah Goldblum · Andrew Wilson
Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning
Jiechao Guan · Zhiwu Lu
Wide Neural Networks Forget Less Catastrophically
Seyed Iman Mirzadeh · Arslan Chaudhry · Dong Yin · Huiyi Hu · Razvan Pascanu · Dilan Gorur · Mehrdad Farajtabar
A Unified View on PAC-Bayes Bounds for Meta-Learning
Arezou Rezazadeh
MAML and ANIL Provably Learn Representations
Liam Collins · Aryan Mokhtari · Sewoong Oh · Sanjay Shakkottai
C-MinHash: Improving Minwise Hashing with Circulant Permutation
Xiaoyun Li · Ping Li
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Samuel Hurault · Arthur Leclaire · Nicolas Papadakis
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Oral
1:30 PM - 3:00 PM
11 Events in this session
Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
Alina Ene · Huy Nguyen
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
Zijian Liu · Ta Duy Nguyen · Alina Ene · Huy Nguyen
Adaptive Second Order Coresets for Data-efficient Machine Learning
Omead Pooladzandi · David Davini · Baharan Mirzasoleiman
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
Trang Tran · Katya Scheinberg · Lam Nguyen
Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models
Valentin Durante · George Katsirelos · Thomas Schiex
Deletion Robust Submodular Maximization over Matroids
PAUL DUETTING · Federico Fusco · Silvio Lattanzi · Ashkan Norouzi-Fard · Morteza Zadimoghaddam
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Xin Yu · Thiago Serra · Srikumar Ramalingam · Shandian Zhe
Instance Dependent Regret Analysis of Kernelized Bandits
Shubhanshu Shekhar · Tara Javidi
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
Shuang Ao · Tianyi Zhou · Jing Jiang · Guodong Long · Xuan Song · Chengqi Zhang
Tell me why! Explanations support learning relational and causal structure
Andrew Lampinen · Nicholas Roy · Ishita Dasgupta · Stephanie Chan · Allison Tam · James McClelland · Chen Yan · Adam Santoro · Neil Rabinowitz · Jane Wang · Feilx Hill
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
Matthias Weissenbacher · Samrath Sinha · Animesh Garg · Yoshinobu Kawahara
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Oral
1:30 PM - 3:00 PM
12 Events in this session
Generalised Policy Improvement with Geometric Policy Composition
Shantanu Thakoor · Mark Rowland · Diana Borsa · Will Dabney · Remi Munos · Andre Barreto
Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine
Divergence-Regularized Multi-Agent Actor-Critic
Kefan Su · Zongqing Lu
Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach
Shuang Wu · Ling Shi · Jun Wang · Guangjian Tian
Off-Policy Reinforcement Learning with Delayed Rewards
Beining Han · Zhizhou Ren · Zuofan Wu · Yuan Zhou · Jian Peng
Direct Behavior Specification via Constrained Reinforcement Learning
Julien Roy · Roger Girgis · Joshua Romoff · Pierre-Luc Bacon · Christopher Pal
Large Batch Experience Replay
Thibault Lahire · Matthieu Geist · Emmanuel Rachelson
Evolving Curricula with Regret-Based Environment Design
Jack Parker-Holder · Minqi Jiang · Michael Dennis · Mikayel Samvelyan · Jakob Foerster · Edward Grefenstette · Tim Rocktäschel
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
Junlin Wu · Yevgeniy Vorobeychik
Transformers are Meta-Reinforcement Learners
Luckeciano Melo
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
Litian Liang · Yaosheng Xu · Stephen Mcaleer · Dailin Hu · Alexander Ihler · Pieter Abbeel · Roy Fox
Constrained Variational Policy Optimization for Safe Reinforcement Learning
Zuxin Liu · Zhepeng Cen · Vladislav Isenbaev · Wei Liu · Steven Wu · Bo Li · Ding Zhao
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Oral
1:30 PM - 3:00 PM
12 Events in this session
Learning Bellman Complete Representations for Offline Policy Evaluation
Jonathan Chang · Kaiwen Wang · Nathan Kallus · Wen Sun
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Nathan Kallus · Xiaojie Mao · Kaiwen Wang · Zhengyuan Zhou
A Simple Reward-free Approach to Constrained Reinforcement Learning
Sobhan Miryoosefi · Chi Jin
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
Jason Yecheng Ma · Andrew Shen · Dinesh Jayaraman · Osbert Bastani
Temporal Difference Learning for Model Predictive Control
Nicklas Hansen · Hao Su · Xiaolong Wang
Model Selection in Batch Policy Optimization
Jonathan Lee · George Tucker · Ofir Nachum · Bo Dai
Adversarially Trained Actor Critic for Offline Reinforcement Learning
Ching-An Cheng · Tengyang Xie · Nan Jiang · Alekh Agarwal
Optimal Estimation of Policy Gradient via Double Fitted Iteration
Chengzhuo Ni · Ruiqi Zhang · Xiang Ji · Xuezhou Zhang · Mengdi Wang
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes
Hongyi Guo · Qi Cai · Yufeng Zhang · Zhuoran Yang · Zhaoran Wang
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Ruiqi Zhang · Xuezhou Zhang · Chengzhuo Ni · Mengdi Wang
Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)
Huang Bojun
On the Role of Discount Factor in Offline Reinforcement Learning
Hao Hu · yiqin yang · Qianchuan Zhao · Chongjie Zhang
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Spotlight
1:30 PM - 3:00 PM
15 Events in this session
Ripple Attention for Visual Perception with Sub-quadratic Complexity
Lin Zheng · Huijie Pan · Lingpeng Kong
Self-supervised Models are Good Teaching Assistants for Vision Transformers
Haiyan Wu · Yuting Gao · Yinqi Zhang · Shaohui Lin · Yuan Xie · Xing Sun · Ke Li
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
Amin Ghiasi · Hamid Kazemi · Steven Reich · Chen Zhu · Micah Goldblum · Tom Goldstein
In defense of dual-encoders for neural ranking
Aditya Menon · Sadeep Jayasumana · Ankit Singh Rawat · Seungyeon Kim · Sashank Jakkam Reddi · Sanjiv Kumar
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
Krzysztof Choromanski · Han Lin · Haoxian Chen · Tianyi Zhang · Arijit Sehanobish · Valerii Likhosherstov · Jack Parker-Holder · Tamas Sarlos · Adrian Weller · Thomas Weingarten
Linear Complexity Randomized Self-attention Mechanism
Lin Zheng · Chong Wang · Lingpeng Kong
Efficient Representation Learning via Adaptive Context Pooling
Chen Huang · Walter Talbott · Navdeep Jaitly · Joshua M Susskind
Toward Compositional Generalization in Object-Oriented World Modeling
Linfeng Zhao · Lingzhi Kong · Robin Walters · Lawson Wong
Fast Population-Based Reinforcement Learning on a Single Machine
Arthur Flajolet · Claire Bizon Monroc · Karim Beguir · Thomas Pierrot
NeuralEF: Deconstructing Kernels by Deep Neural Networks
Zhijie Deng · Jiaxin Shi · Jun Zhu
Visual Attention Emerges from Recurrent Sparse Reconstruction
Baifeng Shi · Yale Song · Neel Joshi · Trevor Darrell · Xin Wang
Transformer Quality in Linear Time
Weizhe Hua · Zihang Dai · Hanxiao Liu · Quoc Le
What Dense Graph Do You Need for Self-Attention?
Yuxin Wang · Chu-Tak Lee · Qipeng Guo · Zhangyue Yin · yunhua zhou · Xuanjing Huang · Xipeng Qiu
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images
Tom Ron · Tamir Hazan
Multi Resolution Analysis (MRA) for Approximate Self-Attention
Zhanpeng Zeng · Sourav Pal · Jeffery Kline · Glenn Fung · Vikas Singh
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Spotlight
1:30 PM - 3:00 PM
14 Events in this session
Learning Stable Classifiers by Transferring Unstable Features
Yujia Bao · Shiyu Chang · Regina Barzilay
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Tianlong Chen · Zhenyu Zhang · Sijia Liu · Yang Zhang · Shiyu Chang · Zhangyang “Atlas” Wang
Attentional Meta-learners for Few-shot Polythetic Classification
Ben Day · Ramon Viñas Torné · Nikola Simidjievski · Pietro Lió
C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra
Yuka Hashimoto · Zhao Wang · Tomoko Matsui
Nonlinear Feature Diffusion on Hypergraphs
Konstantin Prokopchik · Austin Benson · Francesco Tudisco
Kernel Methods for Radial Transformed Compositional Data with Many Zeros
Junyoung Park · Changwon Yoon · Cheolwoo Park · Jeongyoun Ahn
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning
Haoqi Yuan · Zongqing Lu
Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan · Johann Gaebler · Ravi Shroff · Sharad Goel
Fairness with Adaptive Weights
Junyi Chai · Xiaoqian Wang
Understanding Instance-Level Impact of Fairness Constraints
Jialu Wang · Xin Eric Wang · Yang Liu
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
Peizhao Li · Hongfu Liu
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model
Jean-Rémy Conti · Nathan NOIRY · Stephan Clemencon · Vincent Despiegel · Stéphane Gentric
Selective Regression under Fairness Criteria
Abhin Shah · Yuheng Bu · Joshua Lee · Subhro Das · Rameswar Panda · Prasanna Sattigeri · Gregory Wornell
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Jiayin Jin · Zeru Zhang · Yang Zhou · Lingfei Wu
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Spotlight
1:30 PM - 3:00 PM
15 Events in this session
A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan · Jingwu Tang · Yutong Yin · Zhe Feng · Xiang Yan · Manzil Zaheer · Xiaotie Deng
Domain Adaptation for Time Series Forecasting via Attention Sharing
Xiaoyong Jin · Youngsuk Park · Danielle Robinson · Hao Wang · Yuyang Wang
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Nabeel Seedat · Fergus Imrie · Alexis Bellot · Zhaozhi Qian · Mihaela van der Schaar
Disentangling Disease-related Representation from Obscure for Disease Prediction
Chu-ran Wang · Fei Gao · Fandong Zhang · Fangwei Zhong · Yizhou Yu · Yizhou Wang
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
Brandon Trabucco · Xinyang Geng · Aviral Kumar · Sergey Levine
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Seyed Kamyar Seyed Ghasemipour · Satoshi Kataoka · Byron David · Daniel Freeman · Shixiang Gu · Igor Mordatch
Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
Henrique Aguiar · Mauro Santos · Peter Watkinson · Tingting Zhu
Do Differentiable Simulators Give Better Policy Gradients?
Hyung Ju Suh · Max Simchowitz · Kaiqing Zhang · Russ Tedrake
Adaptive Conformal Predictions for Time Series
Margaux Zaffran · Olivier FERON · Yannig Goude · julie Josse · Aymeric Dieuleveut
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Wenlong Huang · Pieter Abbeel · Deepak Pathak · Igor Mordatch
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang · Jiajin Li · Ziqi Gao · Jia Li
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Elvis Nava · John Zhang · Mike Yan Michelis · Tao Du · Pingchuan Ma · Benjamin F. Grewe · Wojciech Matusik · Robert Katzschmann
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Eser Aygün · Ankit Anand · Laurent Orseau · Xavier Glorot · Stephen McAleer · Vlad Firoiu · Lei Zhang · Doina Precup · Shibl Mourad
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
Aviv Netanyahu · Tianmin Shu · Josh Tenenbaum · Pulkit Agrawal
Neural Inverse Kinematic
Raphael Bensadoun · Shir Gur · Nitsan Blau · Lior Wolf
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Spotlight
1:30 PM - 3:00 PM
15 Events in this session
Adversarial Masking for Self-Supervised Learning
Yuge Shi · Siddharth N · Phil Torr · Adam Kosiorek
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
Zhuoning Yuan · Yuexin Wu · Zi-Hao Qiu · Xianzhi Du · Lijun Zhang · Denny Zhou · Tianbao Yang
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Peng Wang · An Yang · Rui Men · Junyang Lin · Shuai Bai · Zhikang Li · Jianxin Ma · Chang Zhou · Jingren Zhou · Hongxia Yang
Multirate Training of Neural Networks
Tiffany Vlaar · Benedict Leimkuhler
Variational Wasserstein gradient flow
Jiaojiao Fan · Qinsheng Zhang · Amirhossein Taghvaei · Yongxin Chen
Building Robust Ensembles via Margin Boosting
Dinghuai Zhang · Hongyang Zhang · Aaron Courville · Yoshua Bengio · Pradeep Ravikumar · Arun Sai Suggala
Investigating Generalization by Controlling Normalized Margin
Alexander Farhang · Jeremy Bernstein · Kushal Tirumala · Yang Liu · Yisong Yue
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
Kendrick Shen · Robbie Jones · Ananya Kumar · Sang Michael Xie · Jeff Z. HaoChen · Tengyu Ma · Percy Liang
VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training
Wangchunshu Zhou · Yan Zeng · shizhe diao · Xinsong Zhang
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Sihang Li · Xiang Wang · An Zhang · Ying-Xin Wu · Xiangnan He · Tat-Seng Chua
Graph Neural Architecture Search Under Distribution Shifts
Yijian Qin · Xin Wang · Ziwei Zhang · Pengtao Xie · Wenwu Zhu
How Powerful are Spectral Graph Neural Networks
Xiyuan Wang · Muhan Zhang
Constraint-based graph network simulator
Yulia Rubanova · Alvaro Sanchez-Gonzalez · Tobias Pfaff · Peter Battaglia
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Zehao Dong · Muhan Zhang · Fuhai Li · Yixin Chen
Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen · Leslie O'Bray · Karsten Borgwardt
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Oral
1:30 PM - 3:00 PM
12 Events in this session
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees
Kimon Antonakopoulos · Dong Quan Vu · Volkan Cevher · Kfir Levy · Panayotis Mertikopoulos
Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints
Martino Bernasconi · Federico Cacciamani · Matteo Castiglioni · Alberto Marchesi · Nicola Gatti · Francesco Trovò
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Luca Carminati · Federico Cacciamani · Marco Ciccone · Nicola Gatti
Exact Learning of Preference Structure: Single-peaked Preferences and Beyond
Sonja Kraiczy · Edith Elkind
Selling Data To a Machine Learner: Pricing via Costly Signaling
Junjie Chen · Minming Li · Haifeng Xu
Hardness and Algorithms for Robust and Sparse Optimization
Eric Price · Sandeep Silwal · Samson Zhou
A Convergent and Dimension-Independent Min-Max Optimization Algorithm
Vijay Keswani · Oren Mangoubi · Sushant Sachdeva · Nisheeth K. Vishnoi
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
Qixin Zhang · Zengde Deng · Zaiyi Chen · Haoyuan Hu · Yu Yang
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
Jungbin Kim · Insoon Yang
The Complexity of k-Means Clustering when Little is Known
Robert Ganian · Thekla Hamm · Viktoriia Korchemna · Karolina Okrasa · Kirill Simonov
Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime
Kyriakos Axiotis ·
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
Peter Richtarik · Igor Sokolov · Elnur Gasanov · Ilyas Fatkhullin · Zhize Li · Eduard Gorbunov
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Registration Desk
2:00 PM - 1:00 AM

Registration Check-in Desk closing at 6 pm. Badge pickup.

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Spotlight
3:30 PM - 5:00 PM
14 Events in this session
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
Hui-Po Wang · Sebastian Stich · Yang He · Mario Fritz
Federated Learning with Label Distribution Skew via Logits Calibration
Jie Zhang · Zhiqi Li · Bo Li · Jianghe Xu · Shuang Wu · Shouhong Ding · Chao Wu
Adaptive Random Walk Gradient Descent for Decentralized Optimization
Tao Sun · Dongsheng Li · Bao Wang
POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging
Shishir G. Patil · Paras Jain · Prabal Dutta · Ion Stoica · Joseph E Gonzalez
Secure Distributed Training at Scale
Eduard Gorbunov · Alexander Borzunov · Michael Diskin · Max Ryabinin
ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD
Karl Bäckström · Marina Papatriantafilou · Philippas Tsigas
Anarchic Federated Learning
Haibo Yang · Xin Zhang · Prashant Khanduri · Jia Liu
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Zhenheng Tang · Yonggang Zhang · Shaohuai Shi · Xin He · Bo Han · Xiaowen Chu
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach
Xuezhou Zhang · Yuda Song · Masatoshi Uehara · Mengdi Wang · Alekh Agarwal · Wen Sun
Sketching Algorithms and Lower Bounds for Ridge Regression
Praneeth Kacham · David Woodruff
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Weichao Mao · Lin Yang · Kaiqing Zhang · Tamer Basar
Utility Theory for Sequential Decision Making
Mehran Shakerinava · Siamak Ravanbakhsh
Online Learning with Knapsacks: the Best of Both Worlds
Matteo Castiglioni · Andrea Celli · Christian Kroer
Optimal Clustering with Noisy Queries via Multi-Armed Bandit
Jinghui Xia · Zengfeng Huang
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Spotlight
3:30 PM - 5:00 PM
15 Events in this session
Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning
Max Paulus · Giulia Zarpellon · Andreas Krause · Laurent Charlin · Chris Maddison
A Regret Minimization Approach to Multi-Agent Control
Udaya Ghai · Udari Madhuhshani · Naomi Leonard · Elad Hazan
Multi-slots Online Matching with High Entropy
XINGYU LU · Qintong Wu · WENLIANG ZHONG
Decision-Focused Learning: Through the Lens of Learning to Rank
Jayanta Mandi · Víctor Bucarey · Maxime Mulamba Ke Tchomba · Tias Guns
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Amrit Singh Bedi · Souradip Chakraborty · Anjaly Parayil · Brian Sadler · Pratap Tokekar · Alec Koppel
Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language
Iou-Jen Liu · Xingdi Yuan · Marc-Alexandre Côté · Pierre-Yves Oudeyer · Alex Schwing
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Adam Villaflor · Zhe Huang · Swapnil Pande · John Dolan · Jeff Schneider
An Analytical Update Rule for General Policy Optimization
Hepeng Li · Nicholas Clavette · Haibo He
Making Linear MDPs Practical via Contrastive Representation Learning
Tianjun Zhang · Tongzheng Ren · Mengjiao Yang · Joseph E Gonzalez · Dale Schuurmans · Bo Dai
Flow-based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen · Yao Mu · Ping Luo · Shengbo Li · Jianyu Chen
A Parametric Class of Approximate Gradient Updates for Policy Optimization
Ramki Gummadi · Saurabh Kumar · Junfeng Wen · Dale Schuurmans
Retrieval-Augmented Reinforcement Learning
Anirudh Goyal · Abe Friesen Friesen · Andrea Banino · Theophane Weber · Nan Rosemary Ke · Adrià Puigdomenech Badia · Arthur Guez · Mehdi Mirza · Peter Humphreys · Ksenia Konyushkova · Michal Valko · Simon Osindero · Timothy Lillicrap · Nicolas Heess · Charles Blundell
Robust Policy Learning over Multiple Uncertainty Sets
Annie Xie · Shagun Sodhani · Chelsea Finn · Joelle Pineau · Amy Zhang
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
Siyi Hu · Chuanlong Xie · Xiaodan Liang · Xiaojun Chang
Learning Dynamics and Generalization in Deep Reinforcement Learning
Clare Lyle · Mark Rowland · Will Dabney · Marta Kwiatkowska · Yarin Gal
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Spotlight
3:30 PM - 5:00 PM
15 Events in this session
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Nan Du · Yanping Huang · Andrew Dai · Simon Tong · Dmitry Lepikhin · Yuanzhong Xu · Maxim Krikun · Yanqi Zhou · Adams Wei Yu · Orhan Firat · Barret Zoph · William Fedus · Maarten Bosma · Zongwei Zhou · Tao Wang · Emma Wang · Kellie Webster · Marie Pellat · Kevin Robinson · Kathleen Meier-Hellstern · Toju Duke · Lucas Dixon · Kun Zhang · Quoc Le · Yonghui Wu · Zhifeng Chen · Claire Cui
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
Mohammad Mahmudul Alam · Edward Raff · Tim Oates · James Holt
Object Permanence Emerges in a Random Walk along Memory
Pavel Tokmakov · Allan Jabri · Jie Li · Adrien Gaidon
Flow-Guided Sparse Transformer for Video Deblurring
Jing Lin · Yuanhao Cai · Xiaowan Hu · Haoqian Wang · Youliang Yan · Xueyi Zou · Henghui Ding · Yulun Zhang · Radu Timofte · Luc Van Gool
N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations
Qingyang Tan · Zherong Pan · Breannan Smith · Takaaki Shiratori · Dinesh Manocha
Staged Training for Transformer Language Models
Sheng Shen · Pete Walsh · Kurt Keutzer · Jesse Dodge · Matthew Peters · Iz Beltagy
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
Martin Genzel · Ingo Gühring · Jan Macdonald · Maximilian März
Self-supervised learning with random-projection quantizer for speech recognition
Chung-Cheng Chiu · James Qin · Yu Zhang · Jiahui Yu · Yonghui Wu
Learning Multiscale Transformer Models for Sequence Generation
Bei Li · Tong Zheng · yi jing · Chengbo Jiao · Tong Xiao · Jingbo Zhu
NP-Match: When Neural Processes meet Semi-Supervised Learning
Jianfeng Wang · Thomas Lukasiewicz · Daniela Massiceti · Xiaolin Hu · Vladimir Pavlovic · Alexandros Neophytou
Proximal and Federated Random Reshuffling
Konstantin Mishchenko · Ahmed Khaled · Peter Richtarik
Federated Learning with Partial Model Personalization
Krishna Pillutla · Kshitiz Malik · Abdel-rahman Mohamed · Michael Rabbat · Maziar Sanjabi · Lin Xiao
A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms
xinwei zhang · Mingyi Hong · Sairaj Dhople · Nicola Elia
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
Yan Huang · Ying Sun · Zehan Zhu · Changzhi Yan · Jinming Xu
Iterative Double Sketching for Faster Least-Squares Optimization
Rui Wang · Yanyan Ouyang · Wangli Xu
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Spotlight
3:30 PM - 5:00 PM
15 Events in this session
Nearly Optimal Catoni’s M-estimator for Infinite Variance
Sujay Bhatt · Guanhua Fang · Ping Li · Gennady Samorodnitsky
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk
Tianrui Chen · Aditya Gangrade · Venkatesh Saligrama
Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis
Oisin Faust · Hamza Fawzi
Contextual Information-Directed Sampling
Botao Hao · Tor Lattimore · Chao Qin
Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
Avishek Ghosh · Abishek Sankararaman
Universal and data-adaptive algorithms for model selection in linear contextual bandits
Vidya Muthukumar · Akshay Krishnamurthy
Regret Minimization with Performative Feedback
Meena Jagadeesan · Tijana Zrnic · Celestine Mendler-Dünner
A Simple yet Universal Strategy for Online Convex Optimization
Lijun Zhang · Guanghui Wang · Jinfeng Yi · Tianbao Yang
Deep Hierarchy in Bandits
Joey Hong · Branislav Kveton · Sumeet Katariya · Manzil Zaheer · Mohammad Ghavamzadeh
Distributionally-Aware Kernelized Bandit Problems for Risk Aversion
Sho Takemori
Asymptotically-Optimal Gaussian Bandits with Side Observations
Alexia Atsidakou · Orestis Papadigenopoulos · Constantine Caramanis · Sujay Sanghavi · Sanjay Shakkottai
Learning from a Learning User for Optimal Recommendations
Fan Yao · Chuanhao Li · Denis Nekipelov · Hongning Wang · Haifeng Xu
Thresholded Lasso Bandit
Kaito Ariu · Kenshi Abe · Alexandre Proutiere
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences
Aadirupa Saha · Pierre Gaillard
Decentralized Online Convex Optimization in Networked Systems
Yiheng Lin · Judy Gan · Guannan Qu · Yash Kanoria · Adam Wierman
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Spotlight
3:30 PM - 5:00 PM
15 Events in this session
Revisiting End-to-End Speech-to-Text Translation From Scratch
Biao Zhang · Barry Haddow · Rico Sennrich
Data Scaling Laws in NMT: The Effect of Noise and Architecture
Yamini Bansal · Behrooz Ghorbani · Ankush Garg · Biao Zhang · Colin Cherry · Behnam Neyshabur · Orhan Firat
Dialog Inpainting: Turning Documents into Dialogs
Zhuyun Dai · Arun Tejasvi Chaganty · Vincent Zhao · Aida Amini · Qazi Mamunur Rashid · Mike Green · Kelvin Guu
Safe Exploration for Efficient Policy Evaluation and Comparison
Runzhe Wan · Branislav Kveton · Rui Song
Adversarial Attacks on Gaussian Process Bandits
Eric Han · Jonathan Scarlett
GALAXY: Graph-based Active Learning at the Extreme
Jifan Zhang · Julian Katz-Samuels · Robert Nowak
When Are Linear Stochastic Bandits Attackable?
Huazheng Wang · Haifeng Xu · Hongning Wang
UniRank: Unimodal Bandit Algorithms for Online Ranking
Camille-Sovanneary GAUTHIER · Romaric Gaudel · Elisa Fromont
Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds
Nate Veldt
Interactive Correlation Clustering with Existential Cluster Constraints
Rico Angell · Nicholas Monath · Nishant Yadav · Andrew McCallum
Simultaneous Graph Signal Clustering and Graph Learning
Abdullah Karaaslanli · Selin Aviyente
Bregman Power k-Means for Clustering Exponential Family Data
Adithya D Vellal · Saptarshi Chakraborty · Jason Xu
SpaceMAP: Visualizing High-Dimensional Data by Space Expansion
Xinrui Zu · Qian Tao
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
Geert-Jan Huizing · Laura Cantini · Gabriel Peyré
Understanding Doubly Stochastic Clustering
Tianjiao Ding · Derek Lim · Rene Vidal · Benjamin Haeffele
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Oral
3:30 PM - 5:00 PM
11 Events in this session
Generalized Strategic Classification and the Case of Aligned Incentives
Sagi Levanon · Nir Rosenfeld
Improving Screening Processes via Calibrated Subset Selection
Luke Lequn Wang · Thorsten Joachims · Manuel Gomez-Rodriguez
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
Lucas Agussurja · Xinyi Xu · Bryan Kian Hsiang Low
Data-SUITE: Data-centric identification of in-distribution incongruous examples
Nabeel Seedat · Jonathan Crabbé · Mihaela van der Schaar
Counterfactual Prediction for Outcome-Oriented Treatments
Hao Zou · Bo Li · Jiangang Han · Shuiping Chen · Xuetao Ding · Peng Cui
Optimal Algorithms for Mean Estimation under Local Differential Privacy
Hilal Asi · Vitaly Feldman · Kunal Talwar
Least Squares Estimation using Sketched Data with Heteroskedastic Errors
Sokbae Lee · Serena Ng
Debiaser Beware: Pitfalls of Centering Regularized Transport Maps
Aram-Alexandre Pooladian · Marco Cuturi · Jonathan Niles-Weed
Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes
Tim Tsz-Kit Lau · Han Liu
Active Nearest Neighbor Regression Through Delaunay Refinement
Alexander Kravberg · Giovanni Luca Marchetti · Vladislav Polianskii · Anastasiia Varava · Florian T. Pokorny · Danica Kragic
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1
Adil Salim · Lukang Sun · Peter Richtarik
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Spotlight
3:30 PM - 5:00 PM
15 Events in this session
Bregman Neural Networks
Jordan Frecon · Gilles Gasso · Massimiliano Pontil · Saverio Salzo
Quantifying and Learning Linear Symmetry-Based Disentanglement
Loek Tonnaer · Luis Armando Perez Rey · Vlado Menkovski · Mike Holenderski · Jacobus Portegies
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
David Knigge · David Romero · Erik Bekkers
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
Zhengyang Shen · Tao Hong · Qi She · Jinwen Ma · Zhouchen Lin
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
Manuel Nonnenmacher · Lukas Oldenburg · Ingo Steinwart · David Reeb
(Non-)Convergence Results for Predictive Coding Networks
Simon Frieder · Thomas Lukasiewicz
Representation Topology Divergence: A Method for Comparing Neural Network Representations.
Serguei Barannikov · Ilya Trofimov · Nikita Balabin · Evgeny Burnaev
Measuring Representational Robustness of Neural Networks Through Shared Invariances
Vedant Nanda · Till Speicher · Camila Kolling · John P Dickerson · Krishna Gummadi · Adrian Weller
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
Kazuki Irie · Robert Cordas · Jürgen Schmidhuber
Flowformer: Linearizing Transformers with Conservation Flows
Haixu Wu · Jialong Wu · Jiehui Xu · Jianmin Wang · Mingsheng Long
Spatial-Channel Token Distillation for Vision MLPs
Yanxi Li · Xinghao Chen · Minjing Dong · Yehui Tang · Yunhe Wang · Chang Xu
Neurocoder: General-Purpose Computation Using Stored Neural Programs
Hung Le · Svetha Venkatesh
Improving Transformers with Probabilistic Attention Keys
Tam Nguyen · Tan Nguyen · Dung Le · Duy Khuong Nguyen · Viet-Anh Tran · Richard Baraniuk · Nhat Ho · Stanley Osher
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Yibing Liu · Haoliang Li · Yangyang Guo · Chenqi KONG · Jing Li · Shiqi Wang
AGNAS: Attention-Guided Micro- and Macro-Architecture Search
Zihao Sun · Yu Hu · Shun Lu · Longxing Yang · Jilin Mei · Yinhe Han · Xiaowei Li
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Spotlight
3:30 PM - 5:00 PM
15 Events in this session
Convergence of Invariant Graph Networks
Chen Cai · Yusu Wang
Rich Feature Construction for the Optimization-Generalization Dilemma
Jianyu Zhang · David Lopez-Paz · Léon Bottou
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Xingcheng Yao · Yanan Zheng · Xiaocong Yang · Zhilin Yang
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems
Zhuangdi Zhu · Junyuan Hong · Steve Drew · Jiayu Zhou
Augment with Care: Contrastive Learning for Combinatorial Problems
Haonan Duan · Pashootan Vaezipoor · Max Paulus · Yangjun Ruan · Chris Maddison
Cycle Representation Learning for Inductive Relation Prediction
Zuoyu Yan · Tengfei Ma · Liangcai Gao · Zhi Tang · Chao Chen
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman · Gabriel Ilharco · Samir Gadre · Rebecca Roelofs · Raphael Gontijo Lopes · Ari Morcos · Hongseok Namkoong · Ali Farhadi · Yair Carmon · Simon Kornblith · Ludwig Schmidt
Do More Negative Samples Necessarily Hurt In Contrastive Learning?
Pranjal Awasthi · Nishanth Dikkala · Pritish Kamath
MetAug: Contrastive Learning via Meta Feature Augmentation
Jiangmeng Li · Wenwen Qiang · Changwen Zheng · Bing Su · Hui Xiong
Investigating Why Contrastive Learning Benefits Robustness against Label Noise
Yihao Xue · Kyle Whitecross · Baharan Mirzasoleiman
Contrastive Learning with Boosted Memorization
Zhihan Zhou · Jiangchao Yao · Yan-Feng Wang · Bo Han · Ya Zhang
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning
Kaiwen Yang · Tianyi Zhou · Xinmei Tian · Dacheng Tao
Interventional Contrastive Learning with Meta Semantic Regularizer
Wenwen Qiang · Jiangmeng Li · Changwen Zheng · Bing Su · Hui Xiong
On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao · Yoshihiro Nagano · Kento Nozawa
Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning
Bobby He · Mete Ozay
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Oral
3:30 PM - 5:00 PM
12 Events in this session
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
Daniil Tiapkin · Denis Belomestny · Eric Moulines · Alexey Naumov · Sergey Samsonov · Yunhao Tang · Michal Valko · Pierre Menard
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
Scott Fujimoto · David Meger · Doina Precup · Ofir Nachum · Shixiang Gu
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
Arnab Kumar Mondal · Vineet Jain · Kaleem Siddiqi · Siamak Ravanbakhsh
Imitation Learning by Estimating Expertise of Demonstrators
Mark Beliaev · Andy Shih · Stefano Ermon · Dorsa Sadigh · Ramtin Pedarsani
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments
Ryan Sullivan · Jordan Terry · Benjamin Black · John P Dickerson
Off-Policy Evaluation for Large Action Spaces via Embeddings
Yuta Saito · Thorsten Joachims
Online Decision Transformer
Qinqing Zheng · Amy Zhang · Aditya Grover
Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training
Jan Kaiser · Oliver Stein · Annika Eichler
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Tianhe (Kevin) Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Chelsea Finn · Sergey Levine
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
Philippe Hansen-Estruch · Amy Zhang · Ashvin Nair · Patrick Yin · Sergey Levine
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Pedro Soto · Ilia Ilmer · Haibin Guan · Jun Li
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
Timothy Castiglia · Anirban Das · Shiqiang Wang · Stacy Patterson
Go to Event Page
Spotlight
3:30 PM - 5:00 PM
14 Events in this session
Global Optimization Networks
Sen Zhao · Erez Louidor · Maya Gupta
Generalized Federated Learning via Sharpness Aware Minimization
Zhe Qu · Xingyu Li · Rui Duan · Yao Liu · Bo Tang · Zhuo Lu
Delay-Adaptive Step-sizes for Asynchronous Learning
Xuyang Wu · Sindri Magnusson · Hamid Reza Feyzmahdavian · Mikael Johansson
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai · Yinwei Dai · Sanjay Singapuram · Jiachen Liu · Xiangfeng Zhu · Harsha Madhyastha · Mosharaf Chowdhury
Learning Augmented Binary Search Trees
Honghao Lin · Tian Luo · David Woodruff
Communication-efficient Distributed Learning for Large Batch Optimization
Rui Liu · Barzan Mozafari
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
Giuseppe Bruno De Luca · Eva Silverstein
A Simple Guard for Learned Optimizers
Isabeau Prémont-Schwarz · Jaroslav Vítků · Jan Feyereisl
An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming
Jihwan Jeong · Parth Jaggi · Andrew Butler · Scott Sanner
Multi-Level Branched Regularization for Federated Learning
Jinkyu Kim · Geeho Kim · Bohyung Han
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
Giulio Franzese · Dimitrios Milios · Maurizio Filippone · Pietro Michiardi
Scaling Structured Inference with Randomization
Yao Fu · John Cunningham · Mirella Lapata
Discrete Tree Flows via Tree-Structured Permutations
Mai Elkady · Jim Lim · David I. Inouye
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
Volodymyr Kuleshov · Shachi Deshpande
Go to Event Page
Reception
5:00 PM - 6:00 PM
Poster
6:00 PM - 8:00 PM
415 Events in this session
Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation
Steffen Schotthöfer · Tianbai Xiao · Martin Frank · Cory Hauck
Composing Partial Differential Equations with Physics-Aware Neural Networks
Matthias Karlbauer · Timothy Praditia · Sebastian Otte · Sergey Oladyshkin · Wolfgang Nowak · Martin V Butz
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Uri Alon · Frank Xu · Junxian He · Sudipta Sengupta · Dan Roth · Graham Neubig
Towards Coherent and Consistent Use of Entities in Narrative Generation
Pinelopi Papalampidi · Kris Cao · Tomas Kocisky
Optimally Controllable Perceptual Lossy Compression
Zeyu Yan · Fei Wen · Peilin Liu
Learning to Solve PDE-constrained Inverse Problems with Graph Networks
QINGQING ZHAO · David B. Lindell · Gordon Wetzstein
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
Yupu Lu · Shijie Lin · Guanqi Chen · Jia Pan
Learning to Estimate and Refine Fluid Motion with Physical Dynamics
Mingrui Zhang · Jianhong Wang · James Tlhomole · Matthew Piggott
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
Manuel Brenner · Florian Hess · Jonas M Mikhaeil · Leonard Bereska · Zahra Monfared · Po-Chen Kuo · Daniel Durstewitz
An Intriguing Property of Geophysics Inversion
Yinan Feng · Yinpeng Chen · Shihang Feng · Peng Jin · Zicheng Liu · Youzuo Lin
Particle Transformer for Jet Tagging
Huilin Qu · Congqiao Li · Sitian Qian
BabelTower: Learning to Auto-parallelized Program Translation
Yuanbo Wen · Qi Guo · Qiang Fu · XiaQing Li · jianxing xu · Yanlin Tang · Yongwei Zhao · Xing Hu · Zidong Du · Ling Li · Chao Wang · Xuehai Zhou · Yunji Chen
ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers
Kaizhi Qian · Yang Zhang · Heting Gao · Junrui Ni · Cheng-I Lai · David Cox · Mark Hasegawa-Johnson · Shiyu Chang
On Distribution Shift in Learning-based Bug Detectors
Jingxuan He · Luca Beurer-Kellner · Martin Vechev
A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan · Jingwu Tang · Yutong Yin · Zhe Feng · Xiang Yan · Manzil Zaheer · Xiaotie Deng
Domain Adaptation for Time Series Forecasting via Attention Sharing
Xiaoyong Jin · Youngsuk Park · Danielle Robinson · Hao Wang · Yuyang Wang
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Nabeel Seedat · Fergus Imrie · Alexis Bellot · Zhaozhi Qian · Mihaela van der Schaar
Disentangling Disease-related Representation from Obscure for Disease Prediction
Chu-ran Wang · Fei Gao · Fandong Zhang · Fangwei Zhong · Yizhou Yu · Yizhou Wang
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
Brandon Trabucco · Xinyang Geng · Aviral Kumar · Sergey Levine
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning
Seyed Kamyar Seyed Ghasemipour · Satoshi Kataoka · Byron David · Daniel Freeman · Shixiang Gu · Igor Mordatch
Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
Henrique Aguiar · Mauro Santos · Peter Watkinson · Tingting Zhu
Do Differentiable Simulators Give Better Policy Gradients?
Hyung Ju Suh · Max Simchowitz · Kaiqing Zhang · Russ Tedrake
Adaptive Conformal Predictions for Time Series
Margaux Zaffran · Olivier FERON · Yannig Goude · julie Josse · Aymeric Dieuleveut
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
Wenlong Huang · Pieter Abbeel · Deepak Pathak · Igor Mordatch
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang · Jiajin Li · Ziqi Gao · Jia Li
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Elvis Nava · John Zhang · Mike Yan Michelis · Tao Du · Pingchuan Ma · Benjamin F. Grewe · Wojciech Matusik · Robert Katzschmann
Proving Theorems using Incremental Learning and Hindsight Experience Replay
Eser Aygün · Ankit Anand · Laurent Orseau · Xavier Glorot · Stephen McAleer · Vlad Firoiu · Lei Zhang · Doina Precup · Shibl Mourad
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
Aviv Netanyahu · Tianmin Shu · Josh Tenenbaum · Pulkit Agrawal
Neural Inverse Kinematic
Raphael Bensadoun · Shir Gur · Nitsan Blau · Lior Wolf
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
Nan Du · Yanping Huang · Andrew Dai · Simon Tong · Dmitry Lepikhin · Yuanzhong Xu · Maxim Krikun · Yanqi Zhou · Adams Wei Yu · Orhan Firat · Barret Zoph · William Fedus · Maarten Bosma · Zongwei Zhou · Tao Wang · Emma Wang · Kellie Webster · Marie Pellat · Kevin Robinson · Kathleen Meier-Hellstern · Toju Duke · Lucas Dixon · Kun Zhang · Quoc Le · Yonghui Wu · Zhifeng Chen · Claire Cui
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
Mohammad Mahmudul Alam · Edward Raff · Tim Oates · James Holt
Object Permanence Emerges in a Random Walk along Memory
Pavel Tokmakov · Allan Jabri · Jie Li · Adrien Gaidon
Flow-Guided Sparse Transformer for Video Deblurring
Jing Lin · Yuanhao Cai · Xiaowan Hu · Haoqian Wang · Youliang Yan · Xueyi Zou · Henghui Ding · Yulun Zhang · Radu Timofte · Luc Van Gool
N-Penetrate: Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations
Qingyang Tan · Zherong Pan · Breannan Smith · Takaaki Shiratori · Dinesh Manocha
Staged Training for Transformer Language Models
Sheng Shen · Pete Walsh · Kurt Keutzer · Jesse Dodge · Matthew Peters · Iz Beltagy
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
Martin Genzel · Ingo Gühring · Jan Macdonald · Maximilian März
Self-supervised learning with random-projection quantizer for speech recognition
Chung-Cheng Chiu · James Qin · Yu Zhang · Jiahui Yu · Yonghui Wu
Learning Multiscale Transformer Models for Sequence Generation
Bei Li · Tong Zheng · yi jing · Chengbo Jiao · Tong Xiao · Jingbo Zhu
NP-Match: When Neural Processes meet Semi-Supervised Learning
Jianfeng Wang · Thomas Lukasiewicz · Daniela Massiceti · Xiaolin Hu · Vladimir Pavlovic · Alexandros Neophytou
Revisiting End-to-End Speech-to-Text Translation From Scratch
Biao Zhang · Barry Haddow · Rico Sennrich
Data Scaling Laws in NMT: The Effect of Noise and Architecture
Yamini Bansal · Behrooz Ghorbani · Ankush Garg · Biao Zhang · Colin Cherry · Behnam Neyshabur · Orhan Firat
Dialog Inpainting: Turning Documents into Dialogs
Zhuyun Dai · Arun Tejasvi Chaganty · Vincent Zhao · Aida Amini · Qazi Mamunur Rashid · Mike Green · Kelvin Guu
Does the Data Induce Capacity Control in Deep Learning?
Rubing Yang · Jialin Mao · Pratik Chaudhari
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming
Chuan Wen · Jianing Qian · Jierui Lin · Jiaye Teng · Dinesh Jayaraman · Yang Gao
Memory-Based Model Editing at Scale
Eric Mitchell · Charles Lin · Antoine Bosselut · Christopher Manning · Chelsea Finn
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
John Rachwan · Daniel Zügner · Bertrand Charpentier · Simon Geisler · Morgane Ayle · Stephan Günnemann
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Guy Hacohen · Avihu Dekel · Daphna Weinshall
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
Byunggook Na · Jisoo Mok · Seongsik Park · Dongjin Lee · Hyeokjun Choe · Sungroh Yoon
Overcoming Oscillations in Quantization-Aware Training
Markus Nagel · Marios Fournarakis · Yelysei Bondarenko · Tijmen Blankevoort
Dataset Condensation via Efficient Synthetic-Data Parameterization
Jang-Hyun Kim · Jinuk Kim · Seong Joon Oh · Sangdoo Yun · Hwanjun Song · Joonhyun Jeong · Jung-Woo Ha · Hyun Oh Song
Searching for BurgerFormer with Micro-Meso-Macro Space Design
Longxing Yang · Yu Hu · Shun Lu · Zihao Sun · Jilin Mei · Yinhe Han · Xiaowei Li
Multi-scale Feature Learning Dynamics: Insights for Double Descent
Mohammad Pezeshki · Amartya Mitra · Yoshua Bengio · Guillaume Lajoie
Dataset Condensation with Contrastive Signals
Saehyung Lee · SANGHYUK CHUN · Sangwon Jung · Sangdoo Yun · Sungroh Yoon
Equivariant Priors for compressed sensing with unknown orientation
Anna Kuzina · Kumar Pratik · Fabio Valerio Massoli · Arash Behboodi
Injecting Logical Constraints into Neural Networks via Straight-Through Estimators
Zhun Yang · Joohyung Lee · Chiyoun Park
Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt
Sören Mindermann · Jan Brauner · Muhammed Razzak · Mrinank Sharma · Andreas Kirsch · Winnie Xu · Benedikt Höltgen · Aidan Gomez · Adrien Morisot · Sebastian Farquhar · Yarin Gal
A Neural Tangent Kernel Perspective of GANs
Jean-Yves Franceschi · Emmanuel de Bézenac · Ibrahim Ayed · Mickael Chen · Sylvain Lamprier · Patrick Gallinari
Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
Jen-Hao Rick Chang · Ashish Shrivastava · Hema Koppula · Xiaoshuai Zhang · Oncel Tuzel
Neural Inverse Transform Sampler
Henry Li · Yuval Kluger
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement
Wengong Jin · Regina Barzilay · Tommi Jaakkola
Diffusion Models for Adversarial Purification
Weili Nie · Brandon Guo · Yujia Huang · Chaowei Xiao · Arash Vahdat · Animashree Anandkumar
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification
Junwen Bai · Shufeng Kong · Carla Gomes
VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis
Tathagat Verma · Abir De · Yateesh Agrawal · Vishwa Vinay · Soumen Chakrabarti
It’s Raw! Audio Generation with State-Space Models
Karan Goel · Albert Gu · Chris Donahue · Christopher Re
Unsupervised Image Representation Learning with Deep Latent Particles
Tal Daniel · Aviv Tamar
Learning Efficient and Robust Ordinary Differential Equations via Invertible Neural Networks
Weiming Zhi · Tin Lai · Lionel Ott · Edwin V Bonilla · Fabio Ramos
Neuro-Symbolic Hierarchical Rule Induction
Claire Glanois · Zhaohui Jiang · Xuening Feng · Paul Weng · Matthieu Zimmer · Dong Li · Wulong Liu · Jianye Hao
General-purpose, long-context autoregressive modeling with Perceiver AR
Curtis Hawthorne · Andrew Jaegle · Cătălina Cangea · Sebastian Borgeaud · Charlie Nash · Mateusz Malinowski · Sander Dieleman · Oriol Vinyals · Matthew Botvinick · Ian Simon · Hannah Sheahan · Neil Zeghidour · Jean-Baptiste Alayrac · Joao Carreira · Jesse Engel
Marginal Tail-Adaptive Normalizing Flows
Mike Laszkiewicz · Johannes Lederer · Asja Fischer
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
Xiang Xu · Karl Willis · Joseph G Lambourne · Chin-Yi Cheng · Pradeep Kumar Jayaraman · Yasutaka Furukawa
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
Shanyan Guan · Huayu Deng · Yunbo Wang · Xiaokang Yang
Equivariance versus Augmentation for Spherical Images
Jan Gerken · Oscar Carlsson · Hampus Linander · Fredrik Ohlsson · Christoffer Petersson · Daniel Persson
Optimal Clipping and Magnitude-aware Differentiation for Improved Quantization-aware Training
Charbel Sakr · Steve Dai · Rangha Venkatesan · Brian Zimmer · William Dally · Brucek Khailany
Neural Network Poisson Models for Behavioural and Neural Spike Train Data
Moein Khajehnejad · Forough Habibollahi · Richard Nock · Ehsan Arabzadeh · Peter Dayan · Amir Dezfouli
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
Huan Zhang · Shiqi Wang · Kaidi Xu · Yihan Wang · Suman Jana · Cho-Jui Hsieh · Zico Kolter
GACT: Activation Compressed Training for Generic Network Architectures
Xiaoxuan Liu · Lianmin Zheng · Dequan Wang · Yukuo Cen · Weize Chen · Xu Han · Jianfei Chen · Zhiyuan Liu · Jie Tang · Joseph Gonzalez · Michael Mahoney · Alvin Cheung
Fast Finite Width Neural Tangent Kernel
Roman Novak · Jascha Sohl-Dickstein · Samuel Schoenholz
G-Mixup: Graph Data Augmentation for Graph Classification
Xiaotian Han · Zhimeng Jiang · Ninghao Liu · Xia Hu
Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models
Beren Millidge · Tommaso Salvatori · Yuhang Song · Thomas Lukasiewicz · Rafal Bogacz
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
Yan Zeng · Xinsong Zhang · Hang Li
Continual Learning with Guarantees via Weight Interval Constraints
Maciej Wołczyk · Karol J. Piczak · Bartosz Wójcik · Łukasz Pustelnik · Paweł Morawiecki · Jacek Tabor · Tomasz Trzcinski · Przemysław Spurek
Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness
Namuk Park · Songkuk Kim
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities
Julian Bitterwolf · Alexander Meinke · Maximilian Augustin · Matthias Hein
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning
Hidetaka Kamigaito · Katsuhiko Hayashi
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Tianlong Chen · Huan Zhang · Zhenyu Zhang · Shiyu Chang · Sijia Liu · Pin-Yu Chen · Zhangyang “Atlas” Wang
Adversarial Masking for Self-Supervised Learning
Yuge Shi · Siddharth N · Phil Torr · Adam Kosiorek
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance
Zhuoning Yuan · Yuexin Wu · Zi-Hao Qiu · Xianzhi Du · Lijun Zhang · Denny Zhou · Tianbao Yang
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Peng Wang · An Yang · Rui Men · Junyang Lin · Shuai Bai · Zhikang Li · Jianxin Ma · Chang Zhou · Jingren Zhou · Hongxia Yang
Multirate Training of Neural Networks
Tiffany Vlaar · Benedict Leimkuhler
Variational Wasserstein gradient flow
Jiaojiao Fan · Qinsheng Zhang · Amirhossein Taghvaei · Yongxin Chen
Building Robust Ensembles via Margin Boosting
Dinghuai Zhang · Hongyang Zhang · Aaron Courville · Yoshua Bengio · Pradeep Ravikumar · Arun Sai Suggala
Investigating Generalization by Controlling Normalized Margin
Alexander Farhang · Jeremy Bernstein · Kushal Tirumala · Yang Liu · Yisong Yue
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
Kendrick Shen · Robbie Jones · Ananya Kumar · Sang Michael Xie · Jeff Z. HaoChen · Tengyu Ma · Percy Liang
VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training
Wangchunshu Zhou · Yan Zeng · shizhe diao · Xinsong Zhang
Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Sihang Li · Xiang Wang · An Zhang · Ying-Xin Wu · Xiangnan He · Tat-Seng Chua
Graph Neural Architecture Search Under Distribution Shifts
Yijian Qin · Xin Wang · Ziwei Zhang · Pengtao Xie · Wenwu Zhu
How Powerful are Spectral Graph Neural Networks
Xiyuan Wang · Muhan Zhang
Constraint-based graph network simulator
Yulia Rubanova · Alvaro Sanchez-Gonzalez · Tobias Pfaff · Peter Battaglia
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Zehao Dong · Muhan Zhang · Fuhai Li · Yixin Chen
Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen · Leslie O'Bray · Karsten Borgwardt
Ripple Attention for Visual Perception with Sub-quadratic Complexity
Lin Zheng · Huijie Pan · Lingpeng Kong
Self-supervised Models are Good Teaching Assistants for Vision Transformers
Haiyan Wu · Yuting Gao · Yinqi Zhang · Shaohui Lin · Yuan Xie · Xing Sun · Ke Li
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
Amin Ghiasi · Hamid Kazemi · Steven Reich · Chen Zhu · Micah Goldblum · Tom Goldstein
In defense of dual-encoders for neural ranking
Aditya Menon · Sadeep Jayasumana · Ankit Singh Rawat · Seungyeon Kim · Sashank Jakkam Reddi · Sanjiv Kumar
From block-Toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked Transformers
Krzysztof Choromanski · Han Lin · Haoxian Chen · Tianyi Zhang · Arijit Sehanobish · Valerii Likhosherstov · Jack Parker-Holder · Tamas Sarlos · Adrian Weller · Thomas Weingarten
Linear Complexity Randomized Self-attention Mechanism
Lin Zheng · Chong Wang · Lingpeng Kong
Efficient Representation Learning via Adaptive Context Pooling
Chen Huang · Walter Talbott · Navdeep Jaitly · Joshua M Susskind
Toward Compositional Generalization in Object-Oriented World Modeling
Linfeng Zhao · Lingzhi Kong · Robin Walters · Lawson Wong
Fast Population-Based Reinforcement Learning on a Single Machine
Arthur Flajolet · Claire Bizon Monroc · Karim Beguir · Thomas Pierrot
NeuralEF: Deconstructing Kernels by Deep Neural Networks
Zhijie Deng · Jiaxin Shi · Jun Zhu
Visual Attention Emerges from Recurrent Sparse Reconstruction
Baifeng Shi · Yale Song · Neel Joshi · Trevor Darrell · Xin Wang
Transformer Quality in Linear Time
Weizhe Hua · Zihang Dai · Hanxiao Liu · Quoc Le
What Dense Graph Do You Need for Self-Attention?
Yuxin Wang · Chu-Tak Lee · Qipeng Guo · Zhangyue Yin · yunhua zhou · Xuanjing Huang · Xipeng Qiu
Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images
Tom Ron · Tamir Hazan
Multi Resolution Analysis (MRA) for Approximate Self-Attention
Zhanpeng Zeng · Sourav Pal · Jeffery Kline · Glenn Fung · Vikas Singh
Dynamic Topic Models for Temporal Document Networks
Delvin Zhang · Hady Lauw
A Functional Information Perspective on Model Interpretation
Itai Gat · Nitay Calderon · Roi Reichart · Tamir Hazan
Be Like Water: Adaptive Floating Point for Machine Learning
Thomas Y. Yeh · Maxwell Sterner · Zerlina Lai · Brandon Chuang · Alexander Ihler
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Johannes Brandstetter · Max Welling · Daniel Worrall
Bregman Neural Networks
Jordan Frecon · Gilles Gasso · Massimiliano Pontil · Saverio Salzo
Quantifying and Learning Linear Symmetry-Based Disentanglement
Loek Tonnaer · Luis Armando Perez Rey · Vlado Menkovski · Mike Holenderski · Jacobus Portegies
Exploiting Redundancy: Separable Group Convolutional Networks on Lie Groups
David Knigge · David Romero · Erik Bekkers
PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs
Zhengyang Shen · Tao Hong · Qi She · Jinwen Ma · Zhouchen Lin
Utilizing Expert Features for Contrastive Learning of Time-Series Representations
Manuel Nonnenmacher · Lukas Oldenburg · Ingo Steinwart · David Reeb
(Non-)Convergence Results for Predictive Coding Networks
Simon Frieder · Thomas Lukasiewicz
Representation Topology Divergence: A Method for Comparing Neural Network Representations.
Serguei Barannikov · Ilya Trofimov · Nikita Balabin · Evgeny Burnaev
Measuring Representational Robustness of Neural Networks Through Shared Invariances
Vedant Nanda · Till Speicher · Camila Kolling · John P Dickerson · Krishna Gummadi · Adrian Weller
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
Kazuki Irie · Robert Cordas · Jürgen Schmidhuber
Flowformer: Linearizing Transformers with Conservation Flows
Haixu Wu · Jialong Wu · Jiehui Xu · Jianmin Wang · Mingsheng Long
Spatial-Channel Token Distillation for Vision MLPs
Yanxi Li · Xinghao Chen · Minjing Dong · Yehui Tang · Yunhe Wang · Chang Xu
Neurocoder: General-Purpose Computation Using Stored Neural Programs
Hung Le · Svetha Venkatesh
Improving Transformers with Probabilistic Attention Keys
Tam Nguyen · Tan Nguyen · Dung Le · Duy Khuong Nguyen · Viet-Anh Tran · Richard Baraniuk · Nhat Ho · Stanley Osher
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Yibing Liu · Haoliang Li · Yangyang Guo · Chenqi KONG · Jing Li · Shiqi Wang
AGNAS: Attention-Guided Micro- and Macro-Architecture Search
Zihao Sun · Yu Hu · Shun Lu · Longxing Yang · Jilin Mei · Yinhe Han · Xiaowei Li
Convergence of Invariant Graph Networks
Chen Cai · Yusu Wang
Rich Feature Construction for the Optimization-Generalization Dilemma
Jianyu Zhang · David Lopez-Paz · Léon Bottou
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Xingcheng Yao · Yanan Zheng · Xiaocong Yang · Zhilin Yang
Resilient and Communication Efficient Learning for Heterogeneous Federated Systems
Zhuangdi Zhu · Junyuan Hong · Steve Drew · Jiayu Zhou
Augment with Care: Contrastive Learning for Combinatorial Problems
Haonan Duan · Pashootan Vaezipoor · Max Paulus · Yangjun Ruan · Chris Maddison
Cycle Representation Learning for Inductive Relation Prediction
Zuoyu Yan · Tengfei Ma · Liangcai Gao · Zhi Tang · Chao Chen
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman · Gabriel Ilharco · Samir Gadre · Rebecca Roelofs · Raphael Gontijo Lopes · Ari Morcos · Hongseok Namkoong · Ali Farhadi · Yair Carmon · Simon Kornblith · Ludwig Schmidt
Do More Negative Samples Necessarily Hurt In Contrastive Learning?
Pranjal Awasthi · Nishanth Dikkala · Pritish Kamath
MetAug: Contrastive Learning via Meta Feature Augmentation
Jiangmeng Li · Wenwen Qiang · Changwen Zheng · Bing Su · Hui Xiong
Investigating Why Contrastive Learning Benefits Robustness against Label Noise
Yihao Xue · Kyle Whitecross · Baharan Mirzasoleiman
Contrastive Learning with Boosted Memorization
Zhihan Zhou · Jiangchao Yao · Yan-Feng Wang · Bo Han · Ya Zhang
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning
Kaiwen Yang · Tianyi Zhou · Xinmei Tian · Dacheng Tao
Interventional Contrastive Learning with Meta Semantic Regularizer
Wenwen Qiang · Jiangmeng Li · Changwen Zheng · Bing Su · Hui Xiong
On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao · Yoshihiro Nagano · Kento Nozawa
Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning
Bobby He · Mete Ozay
Locally Sparse Neural Networks for Tabular Biomedical Data
Junchen Yang · Ofir Lindenbaum · Yuval Kluger
Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification
Jun-Yi Hang · Min-Ling Zhang
Detecting Corrupted Labels Without Training a Model to Predict
Zhaowei Zhu · Zihao Dong · Yang Liu
Prototype-Anchored Learning for Learning with Imperfect Annotations
Xiong Zhou · Xianming Liu · Deming Zhai · Junjun Jiang · Xin Gao · Xiangyang Ji
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
Luc Brogat-Motte · Rémi Flamary · Celine Brouard · Juho Rousu · Florence d'Alché-Buc
Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm
Huayi Tang · Yong Liu
Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network
Shuo Yang · Erkun Yang · Bo Han · Yang Liu · Min Xu · Gang Niu · Tongliang Liu
Invariant Ancestry Search
Phillip Bredahl Mogensen · Nikolaj Thams · Jonas Peters
Unaligned Supervision for Automatic Music Transcription in The Wild
Ben Maman · Amit Bermano
Fourier Learning with Cyclical Data
Yingxiang Yang · Zhihan Xiong · Tianyi Liu · Taiqing Wang · Chong Wang
Linear Adversarial Concept Erasure
Shaul Ravfogel · Michael Twiton · Yoav Goldberg · Ryan Cotterell
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Paul Rolland · Volkan Cevher · Matthäus Kleindessner · Chris Russell · Dominik Janzing · Bernhard Schölkopf · Francesco Locatello
Provable Domain Generalization via Invariant-Feature Subspace Recovery
Haoxiang Wang · Haozhe Si · Bo Li · Han Zhao
Subspace Learning for Effective Meta-Learning
Weisen JIANG · James Kwok · Yu Zhang
Continual Learning via Sequential Function-Space Variational Inference
Tim G. J Rudner · Freddie Bickford Smith · QIXUAN FENG · Yee-Whye Teh · Yarin Gal
Efficient Test-Time Model Adaptation without Forgetting
Shuaicheng Niu · Jiaxiang Wu · Yifan Zhang · Yaofo Chen · Shijian Zheng · Peilin Zhao · Mingkui Tan
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
Lu Bai · Lixin Cui · Edwin Hancock
Leverage Score Sampling for Tensor Product Matrices in Input Sparsity Time
David Woodruff · Amir Zandieh
Random Gegenbauer Features for Scalable Kernel Methods
Insu Han · Amir Zandieh · Haim Avron
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Dong Chen · Lingfei Wu · Siliang Tang · Xiao Yun · Bo Long · Yueting Zhuang
Functional Output Regression with Infimal Convolution: Exploring the Huber and $\epsilon$-insensitive Losses
Alex Lambert · Dimitri Bouche · Zoltan Szabo · Florence d'Alché-Buc
Measuring dissimilarity with diffeomorphism invariance
Théophile Cantelobre · Carlo Ciliberto · Benjamin Guedj · Alessandro Rudi
Importance Weighted Kernel Bayes' Rule
Liyuan Xu · Yutian Chen · Arnaud Doucet · Arthur Gretton
An Asymptotic Test for Conditional Independence using Analytic Kernel Embeddings
Meyer Scetbon · Laurent Meunier · Yaniv Romano
Nyström Kernel Mean Embeddings
Antoine Chatalic · Nicolas Schreuder · Lorenzo Rosasco · Alessandro Rudi
Distribution Regression with Sliced Wasserstein Kernels
Dimitri Marie Meunier · Massimiliano Pontil · Carlo Ciliberto
Learning Stable Classifiers by Transferring Unstable Features
Yujia Bao · Shiyu Chang · Regina Barzilay
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Tianlong Chen · Zhenyu Zhang · Sijia Liu · Yang Zhang · Shiyu Chang · Zhangyang “Atlas” Wang
Attentional Meta-learners for Few-shot Polythetic Classification
Ben Day · Ramon Viñas Torné · Nikola Simidjievski · Pietro Lió
C*-algebra Net: A New Approach Generalizing Neural Network Parameters to C*-algebra
Yuka Hashimoto · Zhao Wang · Tomoko Matsui
Nonlinear Feature Diffusion on Hypergraphs
Konstantin Prokopchik · Austin Benson · Francesco Tudisco
Kernel Methods for Radial Transformed Compositional Data with Many Zeros
Junyoung Park · Changwon Yoon · Cheolwoo Park · Jeongyoun Ahn
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning
Haoqi Yuan · Zongqing Lu
Fast Provably Robust Decision Trees and Boosting
Jun-Qi Guo · Ming-Zhuo Teng · Wei Gao · Zhi-Hua Zhou
Order Constraints in Optimal Transport
Yu Chin Fabian Lim · Laura Wynter · Shiau Hong Lim
Sublinear-Time Clustering Oracle for Signed Graphs
Stefan Neumann · Pan Peng
PAC-Bayesian Bounds on Rate-Efficient Classifiers
Alhabib Abbas · Yiannis Andreopoulos
More Efficient Sampling for Tensor Decomposition With Worst-Case Guarantees
Osman Asif Malik
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Momin Abbas · Quan Xiao · Lisha Chen · Pin-Yu Chen · Tianyi Chen
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum
Hongchang Gao · Junyi Li · Heng Huang
SPDY: Accurate Pruning with Speedup Guarantees
Elias Frantar · Dan Alistarh
Flashlight: Enabling Innovation in Tools for Machine Learning
Jacob Kahn · Vineel Pratap · Tatiana Likhomanenko · Qiantong Xu · Awni Hannun · Jeff Cai · Paden Tomasello · Ann Lee · Edouard Grave · Gilad Avidov · Benoit Steiner · Vitaliy Liptchinsky · Gabriel Synnaeve · Ronan Collobert
On the Robustness of CountSketch to Adaptive Inputs
Edith Cohen · Xin Lyu · Jelani Nelson · Tamas Sarlos · Moshe Shechner · Uri Stemmer
Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning
Jiechao Guan · Zhiwu Lu
Wide Neural Networks Forget Less Catastrophically
Seyed Iman Mirzadeh · Arslan Chaudhry · Dong Yin · Huiyi Hu · Razvan Pascanu · Dilan Gorur · Mehrdad Farajtabar
A Unified View on PAC-Bayes Bounds for Meta-Learning
Arezou Rezazadeh
MAML and ANIL Provably Learn Representations
Liam Collins · Aryan Mokhtari · Sewoong Oh · Sanjay Shakkottai
C-MinHash: Improving Minwise Hashing with Circulant Permutation
Xiaoyun Li · Ping Li
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Samuel Hurault · Arthur Leclaire · Nicolas Papadakis
Safe Exploration for Efficient Policy Evaluation and Comparison
Runzhe Wan · Branislav Kveton · Rui Song
Adversarial Attacks on Gaussian Process Bandits
Eric Han · Jonathan Scarlett
GALAXY: Graph-based Active Learning at the Extreme
Jifan Zhang · Julian Katz-Samuels · Robert Nowak
When Are Linear Stochastic Bandits Attackable?
Huazheng Wang · Haifeng Xu · Hongning Wang
UniRank: Unimodal Bandit Algorithms for Online Ranking
Camille-Sovanneary GAUTHIER · Romaric Gaudel · Elisa Fromont
Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds
Nate Veldt
Interactive Correlation Clustering with Existential Cluster Constraints
Rico Angell · Nicholas Monath · Nishant Yadav · Andrew McCallum
Simultaneous Graph Signal Clustering and Graph Learning
Abdullah Karaaslanli · Selin Aviyente
Bregman Power k-Means for Clustering Exponential Family Data
Adithya D Vellal · Saptarshi Chakraborty · Jason Xu
SpaceMAP: Visualizing High-Dimensional Data by Space Expansion
Xinrui Zu · Qian Tao
Unsupervised Ground Metric Learning Using Wasserstein Singular Vectors
Geert-Jan Huizing · Laura Cantini · Gabriel Peyré
Understanding Doubly Stochastic Clustering
Tianjiao Ding · Derek Lim · Rene Vidal · Benjamin Haeffele
Faster Fundamental Graph Algorithms via Learned Predictions
Justin Chen · Sandeep Silwal · Ali Vakilian · Fred Zhang
Practical Almost-Linear-Time Approximation Algorithms for Hybrid and Overlapping Graph Clustering
Lorenzo Orecchia · Konstantinos Ameranis · Charalampos Tsourakakis · Kunal Talwar
Fair and Fast k-Center Clustering for Data Summarization
Haris Angelidakis · Adam Kurpisz · Leon Sering · Rico Zenklusen
Online and Consistent Correlation Clustering
Vincent Cohen-Addad · Silvio Lattanzi · Andreas Maggiori · Nikos Parotsidis
Generalized Leverage Scores: Geometric Interpretation and Applications
Bruno Ordozgoiti · Antonis Matakos · Aristides Gionis
Adapting k-means Algorithms for Outliers
Christoph Grunau · Vaclav Rozhon
Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization
Karan Chadha · Gary Cheng · John Duchi
Online Algorithms with Multiple Predictions
Keerti Anand · Rong Ge · Amit Kumar · Debmalya Panigrahi
Parsimonious Learning-Augmented Caching
Sungjin Im · Ravi Kumar · Aditya Petety · Manish Purohit
RUMs from Head-to-Head Contests
Matteo Almanza · Flavio Chierichetti · Ravi Kumar · Alessandro Panconesi · Andrew Tomkins
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features
Rahul Mazumder · Xiang Meng · Haoyue Wang
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach
Cedric Malherbe · Kevin Scaman
Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
Alina Ene · Huy Nguyen
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
Zijian Liu · Ta Duy Nguyen · Alina Ene · Huy Nguyen
Adaptive Second Order Coresets for Data-efficient Machine Learning
Omead Pooladzandi · David Davini · Baharan Mirzasoleiman
Nesterov Accelerated Shuffling Gradient Method for Convex Optimization
Trang Tran · Katya Scheinberg · Lam Nguyen
Efficient Low Rank Convex Bounds for Pairwise Discrete Graphical Models
Valentin Durante · George Katsirelos · Thomas Schiex
Deletion Robust Submodular Maximization over Matroids
PAUL DUETTING · Federico Fusco · Silvio Lattanzi · Ashkan Norouzi-Fard · Morteza Zadimoghaddam
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Xin Yu · Thiago Serra · Srikumar Ramalingam · Shandian Zhe
Instance Dependent Regret Analysis of Kernelized Bandits
Shubhanshu Shekhar · Tara Javidi
Proximal and Federated Random Reshuffling
Konstantin Mishchenko · Ahmed Khaled · Peter Richtarik
Federated Learning with Partial Model Personalization
Krishna Pillutla · Kshitiz Malik · Abdel-rahman Mohamed · Michael Rabbat · Maziar Sanjabi · Lin Xiao
A Stochastic Multi-Rate Control Framework For Modeling Distributed Optimization Algorithms
xinwei zhang · Mingyi Hong · Sairaj Dhople · Nicola Elia
Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology
Yan Huang · Ying Sun · Zehan Zhu · Changzhi Yan · Jinming Xu
Iterative Double Sketching for Faster Least-Squares Optimization
Rui Wang · Yanyan Ouyang · Wangli Xu
Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning
Max Paulus · Giulia Zarpellon · Andreas Krause · Laurent Charlin · Chris Maddison
A Regret Minimization Approach to Multi-Agent Control
Udaya Ghai · Udari Madhuhshani · Naomi Leonard · Elad Hazan
Multi-slots Online Matching with High Entropy
XINGYU LU · Qintong Wu · WENLIANG ZHONG
Decision-Focused Learning: Through the Lens of Learning to Rank
Jayanta Mandi · Víctor Bucarey · Maxime Mulamba Ke Tchomba · Tias Guns
Lightweight Projective Derivative Codes for Compressed Asynchronous Gradient Descent
Pedro Soto · Ilia Ilmer · Haibin Guan · Jun Li
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
Timothy Castiglia · Anirban Das · Shiqiang Wang · Stacy Patterson
Least Squares Estimation using Sketched Data with Heteroskedastic Errors
Sokbae Lee · Serena Ng
Debiaser Beware: Pitfalls of Centering Regularized Transport Maps
Aram-Alexandre Pooladian · Marco Cuturi · Jonathan Niles-Weed
Bregman Proximal Langevin Monte Carlo via Bregman--Moreau Envelopes
Tim Tsz-Kit Lau · Han Liu
Active Nearest Neighbor Regression Through Delaunay Refinement
Alexander Kravberg · Giovanni Luca Marchetti · Vladislav Polianskii · Anastasiia Varava · Florian T. Pokorny · Danica Kragic
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1
Adil Salim · Lukang Sun · Peter Richtarik
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
Hui-Po Wang · Sebastian Stich · Yang He · Mario Fritz
Federated Learning with Label Distribution Skew via Logits Calibration
Jie Zhang · Zhiqi Li · Bo Li · Jianghe Xu · Shuang Wu · Shouhong Ding · Chao Wu
Adaptive Random Walk Gradient Descent for Decentralized Optimization
Tao Sun · Dongsheng Li · Bao Wang
POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging
Shishir G. Patil · Paras Jain · Prabal Dutta · Ion Stoica · Joseph E Gonzalez
Secure Distributed Training at Scale
Eduard Gorbunov · Alexander Borzunov · Michael Diskin · Max Ryabinin
ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD
Karl Bäckström · Marina Papatriantafilou · Philippas Tsigas
Anarchic Federated Learning
Haibo Yang · Xin Zhang · Prashant Khanduri · Jia Liu
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Zhenheng Tang · Yonggang Zhang · Shaohuai Shi · Xin He · Bo Han · Xiaowen Chu
Global Optimization Networks
Sen Zhao · Erez Louidor · Maya Gupta
Generalized Federated Learning via Sharpness Aware Minimization
Zhe Qu · Xingyu Li · Rui Duan · Yao Liu · Bo Tang · Zhuo Lu
Delay-Adaptive Step-sizes for Asynchronous Learning
Xuyang Wu · Sindri Magnusson · Hamid Reza Feyzmahdavian · Mikael Johansson
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai · Yinwei Dai · Sanjay Singapuram · Jiachen Liu · Xiangfeng Zhu · Harsha Madhyastha · Mosharaf Chowdhury
Learning Augmented Binary Search Trees
Honghao Lin · Tian Luo · David Woodruff
Communication-efficient Distributed Learning for Large Batch Optimization
Rui Liu · Barzan Mozafari
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
Giuseppe Bruno De Luca · Eva Silverstein
A Simple Guard for Learned Optimizers
Isabeau Prémont-Schwarz · Jaroslav Vítků · Jan Feyereisl
An Exact Symbolic Reduction of Linear Smart Predict+Optimize to Mixed Integer Linear Programming
Jihwan Jeong · Parth Jaggi · Andrew Butler · Scott Sanner
Multi-Level Branched Regularization for Federated Learning
Jinkyu Kim · Geeho Kim · Bohyung Han
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Alexandre Capone · Armin Lederer · Sandra Hirche
Input Dependent Sparse Gaussian Processes
Bahram Jafrasteh · Carlos Villacampa-Calvo · Daniel Hernandez-Lobato
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Da Long · Zheng Wang · Aditi Krishnapriyan · Robert Kirby · Shandian Zhe · Michael Mahoney
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
Konstantinos Kalais · Sotirios Chatzis
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
Zheng Wang · Shandian Zhe
Nonparametric Embeddings of Sparse High-Order Interaction Events
Zheng Wang · Yiming Xu · Conor Tillinghast · Shibo Li · Akil Narayan · Shandian Zhe
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antorán · David Janz · James Allingham · Erik Daxberger · Riccardo Barbano · Eric Nalisnick · Jose Miguel Hernandez-Lobato
NOMU: Neural Optimization-based Model Uncertainty
Jakob Heiss · Jakob Weissteiner · Hanna Wutte · Sven Seuken · Josef Teichmann
Bayesian Model Selection, the Marginal Likelihood, and Generalization
Sanae Lotfi · Pavel Izmailov · Gregory Benton · Micah Goldblum · Andrew Wilson
Revisiting the Effects of Stochasticity for Hamiltonian Samplers
Giulio Franzese · Dimitrios Milios · Maurizio Filippone · Pietro Michiardi
Scaling Structured Inference with Randomization
Yao Fu · John Cunningham · Mirella Lapata
Discrete Tree Flows via Tree-Structured Permutations
Mai Elkady · Jim Lim · David I. Inouye
Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation
Volodymyr Kuleshov · Shachi Deshpande
The Importance of Non-Markovianity in Maximum State Entropy Exploration
Mirco Mutti · Riccardo De Santi · Marcello Restelli
Continuous Control with Action Quantization from Demonstrations
Robert Dadashi · Léonard Hussenot · Damien Vincent · Sertan Girgin · Anton Raichuk · Matthieu Geist · Olivier Pietquin
Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization
Minghuan Liu · Zhengbang Zhu · Yuzheng Zhuang · Weinan Zhang · Jianye Hao · Yong Yu · Jun Wang
Inverse Contextual Bandits: Learning How Behavior Evolves over Time
Alihan Hüyük · Daniel Jarrett · Mihaela van der Schaar
Balancing Sample Efficiency and Suboptimality in Inverse Reinforcement Learning
Angelo Damiani · Giorgio Manganini · Alberto Maria Metelli · Marcello Restelli
Towards Uniformly Superhuman Autonomy via Subdominance Minimization
Brian Ziebart · Sanjiban Choudhury · Xinyan Yan · Paul Vernaza
Causal Imitation Learning under Temporally Correlated Noise
Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu
Interactive Inverse Reinforcement Learning for Cooperative Games
Thomas Kleine Buening · Anne-Marie George · Christos Dimitrakakis
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
Weichao Zhou · Wenchao Li
Robust Imitation Learning against Variations in Environment Dynamics
Jongseong Chae · Seungyul Han · Whiyoung Jung · MYUNG-SIK CHO · Sungho Choi · Youngchul Sung
Discriminator-Weighted Offline Imitation Learning from Suboptimal Demonstrations
Haoran Xu · Xianyuan Zhan · Honglei Yin · Huiling qin
Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation
ZHIHAN LIU · Yufeng Zhang · Zuyue Fu · Zhuoran Yang · Zhaoran Wang
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
Simone Parisi · Aravind Rajeswaran · Senthil Purushwalkam · Abhinav Gupta
COLA: Consistent Learning with Opponent-Learning Awareness
Timon Willi · Alistair Letcher · Johannes Treutlein · Jakob Foerster
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
Wei Xiong · Han Zhong · Chengshuai Shi · Cong Shen · Tong Zhang
A Framework for Learning to Request Rich and Contextually Useful Information from Humans
Khanh Nguyen · Yonatan Bisk · Hal Daumé III
Learning Stochastic Shortest Path with Linear Function Approximation
Yifei Min · Jiafan He · Tianhao Wang · Quanquan Gu
Difference Advantage Estimation for Multi-Agent Policy Gradients
yueheng li · Guangming Xie · Zongqing Lu
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Ling Pan · Longbo Huang · Tengyu Ma · Huazhe Xu
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
Han Zhong · Wei Xiong · Jiyuan Tan · Liwei Wang · Tong Zhang · Zhaoran Wang · Zhuoran Yang
Learning Bellman Complete Representations for Offline Policy Evaluation
Jonathan Chang · Kaiwen Wang · Nathan Kallus · Wen Sun
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Nathan Kallus · Xiaojie Mao · Kaiwen Wang · Zhengyuan Zhou
A Simple Reward-free Approach to Constrained Reinforcement Learning
Sobhan Miryoosefi · Chi Jin
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
Jason Yecheng Ma · Andrew Shen · Dinesh Jayaraman · Osbert Bastani
Temporal Difference Learning for Model Predictive Control
Nicklas Hansen · Hao Su · Xiaolong Wang
Model Selection in Batch Policy Optimization
Jonathan Lee · George Tucker · Ofir Nachum · Bo Dai
Adversarially Trained Actor Critic for Offline Reinforcement Learning
Ching-An Cheng · Tengyang Xie · Nan Jiang · Alekh Agarwal
Optimal Estimation of Policy Gradient via Double Fitted Iteration
Chengzhuo Ni · Ruiqi Zhang · Xiang Ji · Xuezhou Zhang · Mengdi Wang
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes
Hongyi Guo · Qi Cai · Yufeng Zhang · Zhuoran Yang · Zhaoran Wang
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Ruiqi Zhang · Xuezhou Zhang · Chengzhuo Ni · Mengdi Wang
Lagrangian Method for Q-Function Learning (with Applications to Machine Translation)
Huang Bojun
On the Role of Discount Factor in Offline Reinforcement Learning
Hao Hu · yiqin yang · Qianchuan Zhao · Chongjie Zhang
EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning
Shuang Ao · Tianyi Zhou · Jing Jiang · Guodong Long · Xuan Song · Chengqi Zhang
Tell me why! Explanations support learning relational and causal structure
Andrew Lampinen · Nicholas Roy · Ishita Dasgupta · Stephanie Chan · Allison Tam · James McClelland · Chen Yan · Adam Santoro · Neil Rabinowitz · Jane Wang · Feilx Hill
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
Matthias Weissenbacher · Samrath Sinha · Animesh Garg · Yoshinobu Kawahara
Generalised Policy Improvement with Geometric Policy Composition
Shantanu Thakoor · Mark Rowland · Diana Borsa · Will Dabney · Remi Munos · Andre Barreto
Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr Pong · Ashvin Nair · Laura Smith · Catherine Huang · Sergey Levine
Divergence-Regularized Multi-Agent Actor-Critic
Kefan Su · Zongqing Lu
Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach
Shuang Wu · Ling Shi · Jun Wang · Guangjian Tian
Off-Policy Reinforcement Learning with Delayed Rewards
Beining Han · Zhizhou Ren · Zuofan Wu · Yuan Zhou · Jian Peng
Direct Behavior Specification via Constrained Reinforcement Learning
Julien Roy · Roger Girgis · Joshua Romoff · Pierre-Luc Bacon · Christopher Pal
Large Batch Experience Replay
Thibault Lahire · Matthieu Geist · Emmanuel Rachelson
Evolving Curricula with Regret-Based Environment Design
Jack Parker-Holder · Minqi Jiang · Michael Dennis · Mikayel Samvelyan · Jakob Foerster · Edward Grefenstette · Tim Rocktäschel
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
Junlin Wu · Yevgeniy Vorobeychik
Transformers are Meta-Reinforcement Learners
Luckeciano Melo
Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks
Litian Liang · Yaosheng Xu · Stephen Mcaleer · Dailin Hu · Alexander Ihler · Pieter Abbeel · Roy Fox
Constrained Variational Policy Optimization for Safe Reinforcement Learning
Zuxin Liu · Zhepeng Cen · Vladislav Isenbaev · Wei Liu · Steven Wu · Bo Li · Ding Zhao
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Amrit Singh Bedi · Souradip Chakraborty · Anjaly Parayil · Brian Sadler · Pratap Tokekar · Alec Koppel
Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language
Iou-Jen Liu · Xingdi Yuan · Marc-Alexandre Côté · Pierre-Yves Oudeyer · Alex Schwing
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Adam Villaflor · Zhe Huang · Swapnil Pande · John Dolan · Jeff Schneider
An Analytical Update Rule for General Policy Optimization
Hepeng Li · Nicholas Clavette · Haibo He
Making Linear MDPs Practical via Contrastive Representation Learning
Tianjun Zhang · Tongzheng Ren · Mengjiao Yang · Joseph E Gonzalez · Dale Schuurmans · Bo Dai
Flow-based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen · Yao Mu · Ping Luo · Shengbo Li · Jianyu Chen
A Parametric Class of Approximate Gradient Updates for Policy Optimization
Ramki Gummadi · Saurabh Kumar · Junfeng Wen · Dale Schuurmans
Retrieval-Augmented Reinforcement Learning
Anirudh Goyal · Abe Friesen Friesen · Andrea Banino · Theophane Weber · Nan Rosemary Ke · Adrià Puigdomenech Badia · Arthur Guez · Mehdi Mirza · Peter Humphreys · Ksenia Konyushkova · Michal Valko · Simon Osindero · Timothy Lillicrap · Nicolas Heess · Charles Blundell
Robust Policy Learning over Multiple Uncertainty Sets
Annie Xie · Shagun Sodhani · Chelsea Finn · Joelle Pineau · Amy Zhang
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL
Siyi Hu · Chuanlong Xie · Xiaodan Liang · Xiaojun Chang
Learning Dynamics and Generalization in Deep Reinforcement Learning
Clare Lyle · Mark Rowland · Will Dabney · Marta Kwiatkowska · Yarin Gal
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
Daniil Tiapkin · Denis Belomestny · Eric Moulines · Alexey Naumov · Sergey Samsonov · Yunhao Tang · Michal Valko · Pierre Menard
Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error
Scott Fujimoto · David Meger · Doina Precup · Ofir Nachum · Shixiang Gu
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
Arnab Kumar Mondal · Vineet Jain · Kaleem Siddiqi · Siamak Ravanbakhsh
Imitation Learning by Estimating Expertise of Demonstrators
Mark Beliaev · Andy Shih · Stefano Ermon · Dorsa Sadigh · Ramtin Pedarsani
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments
Ryan Sullivan · Jordan Terry · Benjamin Black · John P Dickerson
Off-Policy Evaluation for Large Action Spaces via Embeddings
Yuta Saito · Thorsten Joachims
Online Decision Transformer
Qinqing Zheng · Amy Zhang · Aditya Grover
Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training
Jan Kaiser · Oliver Stein · Annika Eichler
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Tianhe (Kevin) Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Chelsea Finn · Sergey Levine
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
Philippe Hansen-Estruch · Amy Zhang · Ashvin Nair · Patrick Yin · Sergey Levine
A Psychological Theory of Explainability
Scott Cheng-Hsin Yang · Nils Erik Tomas Folke · Patrick Shafto
Task-aware Privacy Preservation for Multi-dimensional Data
Jiangnan Cheng · Ao Tang · Sandeep Chinchali
Strategic Representation
Vineet Nair · Ganesh Ghalme · Inbal Talgam-Cohen · Nir Rosenfeld
Causal Conceptions of Fairness and their Consequences
Hamed Nilforoshan · Johann Gaebler · Ravi Shroff · Sharad Goel
Fairness with Adaptive Weights
Junyi Chai · Xiaoqian Wang
Understanding Instance-Level Impact of Fairness Constraints
Jialu Wang · Xin Eric Wang · Yang Liu
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
Peizhao Li · Hongfu Liu
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model
Jean-Rémy Conti · Nathan NOIRY · Stephan Clemencon · Vincent Despiegel · Stéphane Gentric
Selective Regression under Fairness Criteria
Abhin Shah · Yuheng Bu · Joshua Lee · Subhro Das · Rameswar Panda · Prasanna Sattigeri · Gregory Wornell
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Jiayin Jin · Zeru Zhang · Yang Zhou · Lingfei Wu
Generalized Strategic Classification and the Case of Aligned Incentives
Sagi Levanon · Nir Rosenfeld
Improving Screening Processes via Calibrated Subset Selection
Luke Lequn Wang · Thorsten Joachims · Manuel Gomez-Rodriguez
On the Convergence of the Shapley Value in Parametric Bayesian Learning Games
Lucas Agussurja · Xinyi Xu · Bryan Kian Hsiang Low
Data-SUITE: Data-centric identification of in-distribution incongruous examples
Nabeel Seedat · Jonathan Crabbé · Mihaela van der Schaar
Counterfactual Prediction for Outcome-Oriented Treatments
Hao Zou · Bo Li · Jiangang Han · Shuiping Chen · Xuetao Ding · Peng Cui
Optimal Algorithms for Mean Estimation under Local Differential Privacy
Hilal Asi · Vitaly Feldman · Kunal Talwar
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Andrew Wagenmaker · Yifang Chen · Max Simchowitz · Simon Du · Kevin Jamieson
Generic Coreset for Scalable Learning of Monotonic Kernels: Logistic Regression, Sigmoid and more
Elad Tolochinksy · Ibrahim Jubran · Dan Feldman
Shuffle Private Linear Contextual Bandits
Sayak Ray Chowdhury · Xingyu Zhou
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity
Laixi Shi · Gen Li · Yuting Wei · Yuxin Chen · Yuejie Chi
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
Andrew Wagenmaker · Yifang Chen · Max Simchowitz · Simon Du · Kevin Jamieson
Label Ranking through Nonparametric Regression
Dimitris Fotakis · Alkis Kalavasis · Eleni Psaroudaki
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
Dan Qiao · Ming Yin · Ming Min · Yu-Xiang Wang
A Simple Unified Framework for High Dimensional Bandit Problems
Wenjie Li · Adarsh Barik · Jean Honorio
A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds
Jiahao He · Jiheng Zhang · Rachel Q. Zhang
Branching Reinforcement Learning
Yihan Du · Wei Chen
Fast rates for noisy interpolation require rethinking the effect of inductive bias
Konstantin Donhauser · Nicolò Ruggeri · Stefan Stojanovic · Fanny Yang
Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path
Haoyuan Cai · Tengyu Ma · Simon Du
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
sajad khodadadian · PRANAY SHARMA · Gauri Joshi · Siva Maguluri
Entropic Gromov-Wasserstein between Gaussian Distributions
Khang Le · Dung Le · Huy Nguyen · · Tung Pham · Nhat Ho
No-Regret Learning in Partially-Informed Auctions
Wenshuo Guo · Michael Jordan · Ellen Vitercik
On Last-Iterate Convergence Beyond Zero-Sum Games
Ioannis Anagnostides · Ioannis Panageas · Gabriele Farina · Tuomas Sandholm
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
Gabriele Farina · Chung-Wei Lee · Haipeng Luo · Christian Kroer
Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games
Lucas Baudin · Rida Laraki
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming
Gregory Dexter · Agniva Chowdhury · Haim Avron · Petros Drineas
Nested Bandits
Matthieu Martin · Panayotis Mertikopoulos · Thibaud J Rahier · Houssam Zenati
Information Discrepancy in Strategic Learning
Yahav Bechavod · Chara Podimata · Steven Wu · Juba Ziani
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees
Kimon Antonakopoulos · Dong Quan Vu · Volkan Cevher · Kfir Levy · Panayotis Mertikopoulos
Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints
Martino Bernasconi · Federico Cacciamani · Matteo Castiglioni · Alberto Marchesi · Nicola Gatti · Francesco Trovò
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Luca Carminati · Federico Cacciamani · Marco Ciccone · Nicola Gatti
Exact Learning of Preference Structure: Single-peaked Preferences and Beyond
Sonja Kraiczy · Edith Elkind
Selling Data To a Machine Learner: Pricing via Costly Signaling
Junjie Chen · Minming Li · Haifeng Xu
Hardness and Algorithms for Robust and Sparse Optimization
Eric Price · Sandeep Silwal · Samson Zhou
A Convergent and Dimension-Independent Min-Max Optimization Algorithm
Vijay Keswani · Oren Mangoubi · Sushant Sachdeva · Nisheeth K. Vishnoi
Stochastic Continuous Submodular Maximization: Boosting via Non-oblivious Function
Qixin Zhang · Zengde Deng · Zaiyi Chen · Haoyuan Hu · Yu Yang
Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
Jungbin Kim · Insoon Yang
The Complexity of k-Means Clustering when Little is Known
Robert Ganian · Thekla Hamm · Viktoriia Korchemna · Karolina Okrasa · Kirill Simonov
Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime
Kyriakos Axiotis ·
3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation
Peter Richtarik · Igor Sokolov · Elnur Gasanov · Ilyas Fatkhullin · Zhize Li · Eduard Gorbunov
Nearly Optimal Catoni’s M-estimator for Infinite Variance
Sujay Bhatt · Guanhua Fang · Ping Li · Gennady Samorodnitsky
Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk
Tianrui Chen · Aditya Gangrade · Venkatesh Saligrama
Local Linear Convergence of Douglas-Rachford for Linear Programming: a Probabilistic Analysis
Oisin Faust · Hamza Fawzi
Contextual Information-Directed Sampling
Botao Hao · Tor Lattimore · Chao Qin
Breaking the $\sqrt{T}$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
Avishek Ghosh · Abishek Sankararaman
Universal and data-adaptive algorithms for model selection in linear contextual bandits
Vidya Muthukumar · Akshay Krishnamurthy
Regret Minimization with Performative Feedback
Meena Jagadeesan · Tijana Zrnic · Celestine Mendler-Dünner
A Simple yet Universal Strategy for Online Convex Optimization
Lijun Zhang · Guanghui Wang · Jinfeng Yi · Tianbao Yang
Deep Hierarchy in Bandits
Joey Hong · Branislav Kveton · Sumeet Katariya · Manzil Zaheer · Mohammad Ghavamzadeh
Distributionally-Aware Kernelized Bandit Problems for Risk Aversion
Sho Takemori
Asymptotically-Optimal Gaussian Bandits with Side Observations
Alexia Atsidakou · Orestis Papadigenopoulos · Constantine Caramanis · Sujay Sanghavi · Sanjay Shakkottai
Learning from a Learning User for Optimal Recommendations
Fan Yao · Chuanhao Li · Denis Nekipelov · Hongning Wang · Haifeng Xu
Thresholded Lasso Bandit
Kaito Ariu · Kenshi Abe · Alexandre Proutiere
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences
Aadirupa Saha · Pierre Gaillard
Decentralized Online Convex Optimization in Networked Systems
Yiheng Lin · Judy Gan · Guannan Qu · Yash Kanoria · Adam Wierman
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach
Xuezhou Zhang · Yuda Song · Masatoshi Uehara · Mengdi Wang · Alekh Agarwal · Wen Sun
Sketching Algorithms and Lower Bounds for Ridge Regression
Praneeth Kacham · David Woodruff
On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning
Weichao Mao · Lin Yang · Kaiqing Zhang · Tamer Basar
Utility Theory for Sequential Decision Making
Mehran Shakerinava · Siamak Ravanbakhsh
Online Learning with Knapsacks: the Best of Both Worlds
Matteo Castiglioni · Andrea Celli · Christian Kroer
Optimal Clustering with Noisy Queries via Multi-Armed Bandit
Jinghui Xia · Zengfeng Huang
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Mental Health in ML Academia

Paula Gradu · Cyril Zhang
7:00 PM - 9:00 PM

A roundtable participant-led discussion on how to ensure the mental wellbeing of our community: from preventing and dealing with burnout and maintaining a healthy work-life balance to brainstorming ways in which we could better support peers struggling with their mental health or advising situations. While we will be discussing possibly heavy topics, the focus will be on the positive actions we can all take to foster a mentally healthy ML academic community.

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7:00 PM - 9:00 PM

Enjoy an evening full of fun and wisdom with 8 leading AI/ML scientists and engineers to debate whether progress towards achieving AI will be mostly driven by engineering or science. You will be joined by Pulkit Agrawal (MIT), François Charton (Meta), Kyunghyun Cho (NYU), Sujoy Ganguly (Unity Technologies), Maya Gupta (Didero), Been Kim (MIT), Ida Momennejad (Microsoft), and Sella Nevo (Google) who will debate the topic following the British Parliament Style. We will also host live votes right before and after the debate to see whether you are convinced by our debaters!

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