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Oral
Tue Jul 19 07:30 AM -- 07:50 AM (PDT) @ Hall G
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them
In Deep Learning: Robustness
Florian Tramer
Oral
Tue Jul 19 07:30 AM -- 07:50 AM (PDT) @ Room 307
Exact Optimal Accelerated Complexity for Fixed-Point Iterations
In Optimization: Convex
Jisun Park · Ernest Ryu
Oral
Tue Jul 19 07:30 AM -- 07:50 AM (PDT) @ Room 309
Online Learning for Min Sum Set Cover and Pandora’s Box
In Theory: Online Learning/Bandits
Evangelia Gergatsouli · Christos Tzamos
Oral
Tue Jul 19 07:30 AM -- 07:50 AM (PDT) @ Ballroom 3 & 4
Tackling covariate shift with node-based Bayesian neural networks
In Probabilistic Methods/Applications
Trung Trinh · Markus Heinonen · Luigi Acerbi · Samuel Kaski
Oral
Tue Jul 19 08:00 AM -- 08:20 AM (PDT) @ Hall F
Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP
In Reinforcement Learning
Liyu Chen · Rahul Jain · Haipeng Luo
Oral
Tue Jul 19 08:00 AM -- 08:20 AM (PDT) @ Room 301 - 303 None
Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information
In APP: Language, Speech and Dialog
Kawin Ethayarajh · Yejin Choi · Swabha Swayamdipta
Oral
Tue Jul 19 08:05 AM -- 08:25 AM (PDT) @ Room 327 - 329
Unified Scaling Laws for Routed Language Models
In Deep Learning
Aidan Clark · Diego de Las Casas · Aurelia Guy · Arthur Mensch · Michela Paganini · Jordan Hoffmann · Bogdan Damoc · Blake Hechtman · Trevor Cai · Sebastian Borgeaud · George van den Driessche · Eliza Rutherford · Tom Hennigan · Matthew Johnson · Albin Cassirer · Chris Jones · Elena Buchatskaya · David Budden · Laurent Sifre · Simon Osindero · Oriol Vinyals · Marc'Aurelio Ranzato · Jack Rae · Erich Elsen · Koray Kavukcuoglu · Karen Simonyan
Oral
Tue Jul 19 08:05 AM -- 08:25 AM (PDT) @ Ballroom 1 & 2
Bounding Training Data Reconstruction in Private (Deep) Learning
In Social Aspects
Chuan Guo · Brian Karrer · Kamalika Chaudhuri · Laurens van der Maaten
Oral
Tue Jul 19 08:05 AM -- 08:25 AM (PDT) @ Room 318 - 320
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning
In Transfer/Multitask/Meta Learning
Utku Evci · Vincent Dumoulin · Hugo Larochelle · Michael Mozer
Oral
Tue Jul 19 08:05 AM -- 08:25 AM (PDT) @ Room 310
Equivariant Diffusion for Molecule Generation in 3D
In Deep Learning: Generative Models/Autoencoders
Emiel Hoogeboom · Victor Garcia Satorras · Clément Vignac · Max Welling
Oral
Tue Jul 19 08:10 AM -- 08:30 AM (PDT) @ Ballroom 3 & 4
Tractable Uncertainty for Structure Learning
In Probabilistic Methods/Applications
Benjie Wang · Matthew Wicker · Marta Kwiatkowska
Oral
Tue Jul 19 08:10 AM -- 08:30 AM (PDT) @ Hall G
To Smooth or Not? When Label Smoothing Meets Noisy Labels
In Deep Learning: Robustness
Jiaheng Wei · Hangyu Liu · Tongliang Liu · Gang Niu · Masashi Sugiyama · Yang Liu
Oral
Tue Jul 19 08:15 AM -- 08:35 AM (PDT) @ Room 309
Batched Dueling Bandits
In Theory: Online Learning/Bandits
Arpit Agarwal · Rohan Ghuge · viswanath nagarajan
Oral
Tue Jul 19 08:15 AM -- 08:35 AM (PDT) @ Room 307
Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems
In Optimization: Convex
Jaewook Suh · Gyumin Roh · Ernest Ryu
Oral
Tue Jul 19 10:30 AM -- 10:50 AM (PDT) @ Room 309
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
In Reinforcement Learning: Deep/Batch/Offline
Vihang Patil · Markus Hofmarcher · Marius-Constantin Dinu · Matthias Dorfer · Patrick Blies · Johannes Brandstetter · Jose A. Arjona-Medina · Sepp Hochreiter
Oral
Tue Jul 19 10:30 AM -- 10:50 AM (PDT) @ Room 310
Robustness Implies Generalization via Data-Dependent Generalization Bounds
In Theory: Bandits/RL/Everything Else
Kenji Kawaguchi · Zhun Deng · Kyle Luh · Jiaoyang Huang
Oral
Tue Jul 19 10:30 AM -- 10:50 AM (PDT) @ Room 318 - 320
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
In Optimization
Sixing Yu · Arya Mazaheri · Ali Jannesari
Oral
Tue Jul 19 10:30 AM -- 10:50 AM (PDT) @ Hall G None
Learning Mixtures of Linear Dynamical Systems
In Theory
Yanxi Chen · H. Vincent Poor
Oral
Tue Jul 19 11:00 AM -- 11:20 AM (PDT) @ Room 307
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
In PM: Gaussian Processes
Jonathan Wenger · Geoff Pleiss · Philipp Hennig · John Cunningham · Jacob Gardner
Oral
Tue Jul 19 11:00 AM -- 11:20 AM (PDT) @ Room 301 - 303
Online Active Regression
In MISC: Unsupervised and Semi-supervised Learning
Cheng Chen · Yi Li · Yiming Sun
Oral
Tue Jul 19 11:00 AM -- 11:20 AM (PDT) @ Ballroom 1 & 2 None
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
In DL: Algorithms
Tri Dao · Beidi Chen · Nimit Sohoni · Arjun Desai · Michael Poli · Jessica Grogan · Alexander Liu · Aniruddh Rao · Atri Rudra · Christopher Re
Oral
Tue Jul 19 11:00 AM -- 11:20 AM (PDT) @ Hall F
Path-Gradient Estimators for Continuous Normalizing Flows
In APP: Computer Vision
Lorenz Vaitl · Kim A. Nicoli · Shinichi Nakajima · Pan Kessel
Oral
Tue Jul 19 11:05 AM -- 11:25 AM (PDT) @ Room 327 - 329
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
In DL: Graph Neural Networks
Alexei Baevski · Wei-Ning Hsu · Qiantong Xu · Arun Babu · Jiatao Gu · Michael Auli
Oral
Tue Jul 19 11:05 AM -- 11:25 AM (PDT) @ Ballroom 3 & 4
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four
In SA: Accountability, Transparency and Interpretability
Stephan Wäldchen · Sebastian Pokutta · Felix Huber
Oral
Tue Jul 19 11:10 AM -- 11:30 AM (PDT) @ Room 318 - 320 None
Solving Stackelberg Prediction Game with Least Squares Loss via Spherically Constrained Least Squares Reformulation
In Optimization
Jiali Wang · Wen Huang · Rujun Jiang · Xudong Li · Alex Wang
Oral
Tue Jul 19 11:15 AM -- 11:35 AM (PDT) @ Hall G
Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model
In Theory
Heejong Bong · Alessandro Rinaldo
Oral
Tue Jul 19 11:15 AM -- 11:35 AM (PDT) @ Room 309
Offline RL Policies Should Be Trained to be Adaptive
In Reinforcement Learning: Deep/Batch/Offline
Dibya Ghosh · Anurag Ajay · Pulkit Agrawal · Sergey Levine
Oral
Tue Jul 19 11:15 AM -- 11:35 AM (PDT) @ Room 310
Individual Preference Stability for Clustering
In Theory: Bandits/RL/Everything Else
Saba Ahmadi · Pranjal Awasthi · Samir Khuller · Matthäus Kleindessner · Jamie Morgenstern · Pattara Sukprasert · Ali Vakilian
Oral
Tue Jul 19 01:15 PM -- 01:35 PM (PDT) @ Hall G
H-Consistency Bounds for Surrogate Loss Minimizers
In T: Learning/Deep Learning Theory
Pranjal Awasthi · Anqi Mao · Mehryar Mohri · Yutao Zhong
Oral
Tue Jul 19 01:15 PM -- 01:35 PM (PDT) @ Room 310
Cooperative Online Learning in Stochastic and Adversarial MDPs
In Theory
Tal Lancewicki · Aviv Rosenberg · Yishay Mansour
Oral
Tue Jul 19 01:15 PM -- 01:35 PM (PDT) @ Hall F
POEM: Out-of-Distribution Detection with Posterior Sampling
In SA: Trustworthy Machine Learning
Yifei Ming · Ying Fan · Yixuan Li
Oral
Tue Jul 19 01:15 PM -- 01:35 PM (PDT) @ Room 307
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
In PM: Monte Carlo and Sampling Methods
Insu Han · Mike Gartrell · Elvis Dohmatob · Amin Karbasi
Oral
Tue Jul 19 01:45 PM -- 02:05 PM (PDT) @ Room 318 - 320
Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence
In RL: Multi-agent
Dongsheng Ding · Chen-Yu Wei · Kaiqing Zhang · Mihailo Jovanovic
Oral
Tue Jul 19 01:45 PM -- 02:05 PM (PDT) @ Room 309
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
In OPT: Non-Convex
Davoud Ataee Tarzanagh · Mingchen Li · Christos Thrampoulidis · Samet Oymak
Oral
Tue Jul 19 01:45 PM -- 02:05 PM (PDT) @ Room 327 - 329
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain
In DL: Sequential Models
Samuel Holt · Zhaozhi Qian · Mihaela van der Schaar
Oral
Tue Jul 19 01:45 PM -- 02:05 PM (PDT) @ Room 301 - 303 None
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
In APP: Neuroscience, Cognitive Science
Adam Foster · Arpi Vezer · Craig Glastonbury · Páidí Creed · Sam Abujudeh · Aaron Sim
Oral
Tue Jul 19 01:50 PM -- 02:10 PM (PDT) @ Ballroom 1 & 2
Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations
In Deep Learning
Michael Zhang · Nimit Sohoni · Hongyang Zhang · Chelsea Finn · Christopher Re
Oral
Tue Jul 19 01:50 PM -- 02:10 PM (PDT) @ Ballroom 3 & 4 None
Minimum Cost Intervention Design for Causal Effect Identification
In MISC: Causality
Sina Akbari · Jalal Etesami · Negar Kiyavash
Oral
Tue Jul 19 01:55 PM -- 02:15 PM (PDT) @ Hall F
Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems
In SA: Trustworthy Machine Learning
Yue Gao · Ilia Shumailov · Kassem Fawaz
Oral
Tue Jul 19 02:00 PM -- 02:20 PM (PDT) @ Hall G
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models
In T: Learning/Deep Learning Theory
Tudor Manole · Nhat Ho
Oral
Tue Jul 19 02:00 PM -- 02:20 PM (PDT) @ Room 310
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces
In Theory
Yinglun Zhu · Paul Mineiro
Oral
Tue Jul 19 02:00 PM -- 02:20 PM (PDT) @ Room 307
Nonparametric Involutive Markov Chain Monte Carlo
In PM: Monte Carlo and Sampling Methods
Carol Mak · Fabian Zaiser · Luke Ong
Oral
Wed Jul 20 07:30 AM -- 07:50 AM (PDT) @ Room 310
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
In T: Game Theory/RL/Planning
Chengchun Shi · Masatoshi Uehara · Jiawei Huang · Nan Jiang
Oral
Wed Jul 20 07:30 AM -- 07:50 AM (PDT) @ Room 301 - 303
How Tempering Fixes Data Augmentation in Bayesian Neural Networks
In PM: Variational Inference/Bayesian Models and Methods
Gregor Bachmann · Lorenzo Noci · Thomas Hofmann
Oral
Wed Jul 20 07:30 AM -- 07:50 AM (PDT) @ Room 327 - 329
Adapting to Mixing Time in Stochastic Optimization with Markovian Data
In OPT: First Order
Ron Dorfman · Kfir Levy
Oral
Wed Jul 20 08:00 AM -- 08:20 AM (PDT) @ Hall F
A new similarity measure for covariate shift with applications to nonparametric regression
In T: Learning Theory/Domain Adaptation
Reese Pathak · Cong Ma · Martin Wainwright
Oral
Wed Jul 20 08:05 AM -- 08:25 AM (PDT) @ Room 309
Robust Training of Neural Networks Using Scale Invariant Architectures
In DL: Theory
Zhiyuan Li · Srinadh Bhojanapalli · Manzil Zaheer · Sashank Jakkam Reddi · Sanjiv Kumar
Oral
Wed Jul 20 08:05 AM -- 08:25 AM (PDT) @ Room 318 - 320 None
Privacy for Free: How does Dataset Condensation Help Privacy?
In Social Aspects/MISC
Tian Dong · Bo Zhao · Lingjuan Lyu
Oral
Wed Jul 20 08:05 AM -- 08:25 AM (PDT) @ Room 307
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer
In Reinforcement Learning: Deep RL
Xingyu Liu · Deepak Pathak · Kris Kitani
Oral
Wed Jul 20 08:05 AM -- 08:25 AM (PDT) @ Ballroom 1 & 2
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
In Deep Learning
Zeke Xie · Xinrui Wang · Huishuai Zhang · Issei Sato · Masashi Sugiyama
Oral
Wed Jul 20 08:05 AM -- 08:25 AM (PDT) @ Hall G None
Learning inverse folding from millions of predicted structures
In Applications
Chloe Hsu · Robert Verkuil · Jason Liu · Zeming Lin · Brian Hie · Tom Sercu · Adam Lerer · Alexander Rives
Oral
Wed Jul 20 08:05 AM -- 08:25 AM (PDT) @ Ballroom 3 & 4 None
Stable Conformal Prediction Sets
In MISC: General Machine Learning Techniques
Eugene Ndiaye
Oral
Wed Jul 20 08:10 AM -- 08:30 AM (PDT) @ Room 310
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
In T: Game Theory/RL/Planning
Qinghua Liu · Yuanhao Wang · Chi Jin
Oral
Wed Jul 20 08:15 AM -- 08:35 AM (PDT) @ Room 301 - 303
BAMDT: Bayesian Additive Semi-Multivariate Decision Trees for Nonparametric Regression
In PM: Variational Inference/Bayesian Models and Methods
Zhao Tang Luo · Huiyan Sang · Bani Mallick
Oral
Wed Jul 20 08:15 AM -- 08:35 AM (PDT) @ Room 327 - 329
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
In OPT: First Order
Sharan Vaswani · Benjamin Dubois-Taine · Reza Babanezhad
Oral
Wed Jul 20 10:15 AM -- 10:35 AM (PDT) @ Room 301 - 303
Bayesian Continuous-Time Tucker Decomposition
In PM: Bayesian Models and Methods
Shikai Fang · Akil Narayan · Robert Kirby · Shandian Zhe
Oral
Wed Jul 20 10:15 AM -- 10:35 AM (PDT) @ Room 309
Tight and Robust Private Mean Estimation with Few Users
In SA: Trustworthy Machine Learning
Shyam Narayanan · Vahab Mirrokni · Hossein Esfandiari
Oral
Wed Jul 20 10:15 AM -- 10:35 AM (PDT) @ Room 327 - 329
Generative Trees: Adversarial and Copycat
In T: Online Learning and Bandits/Learning Theory
Richard Nock · Mathieu Guillame-Bert
Oral
Wed Jul 20 10:45 AM -- 11:05 AM (PDT) @ Room 307
Planning with Diffusion for Flexible Behavior Synthesis
In Reinforcement Learning
Michael Janner · Yilun Du · Josh Tenenbaum · Sergey Levine
Oral
Wed Jul 20 10:45 AM -- 11:05 AM (PDT) @ Hall G
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests
In MISC: Representation Learning/Causality
Victor Chernozhukov · Whitney Newey · Víctor Quintas-Martínez · Vasilis Syrgkanis
Oral
Wed Jul 20 10:45 AM -- 11:05 AM (PDT) @ Hall F
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
In APP: Chemistry and Drug Discovery
Yinan Huang · Xingang Peng · Jianzhu Ma · Muhan Zhang
Oral
Wed Jul 20 10:50 AM -- 11:10 AM (PDT) @ Room 318 - 320
A Dynamical System Perspective for Lipschitz Neural Networks
In DL: Robustness
Laurent Meunier · Blaise Delattre · Alexandre ARAUJO · Alexandre Allauzen
Oral
Wed Jul 20 10:50 AM -- 11:10 AM (PDT) @ Ballroom 1 & 2
Generating 3D Molecules for Target Protein Binding
In Deep Learning/APP:Computer Vision
Meng Liu · Youzhi Luo · Kanji Uchino · Koji Maruhashi · Shuiwang Ji
Oral
Wed Jul 20 10:50 AM -- 11:10 AM (PDT) @ Ballroom 3 & 4 None
Active fairness auditing
In Theory
Tom Yan · Chicheng Zhang
Oral
Wed Jul 20 10:50 AM -- 11:10 AM (PDT) @ Room 310
Robustness Verification for Contrastive Learning
In DL: Robustness
Zekai Wang · Weiwei Liu
Oral
Wed Jul 20 11:00 AM -- 11:20 AM (PDT) @ Room 309
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
In SA: Trustworthy Machine Learning
Gautam Kamath · Xingtu Liu · Huanyu Zhang
Oral
Wed Jul 20 11:00 AM -- 11:20 AM (PDT) @ Room 327 - 329
Agnostic Learnability of Halfspaces via Logistic Loss
In T: Online Learning and Bandits/Learning Theory
Ziwei Ji · Kwangjun Ahn · Pranjal Awasthi · Satyen Kale · Stefani Karp
Oral
Wed Jul 20 11:00 AM -- 11:20 AM (PDT) @ Room 301 - 303
Function-space Inference with Sparse Implicit Processes
In PM: Bayesian Models and Methods
Simon R Santana · Bryan Zaldivar · Daniel Hernandez-Lobato
Oral
Wed Jul 20 02:00 PM -- 02:20 PM (PDT) @ Room 327 - 329
A General Recipe for Likelihood-free Bayesian Optimization
In Deep Learning/Optimization
Jiaming Song · Lantao Yu · Willie Neiswanger · Stefano Ermon
Oral
Wed Jul 20 02:00 PM -- 02:20 PM (PDT) @ Room 310
Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models.
In Deep Learning/MISC
Abhineet Agarwal · Yan Shuo Tan · Omer Ronen · Chandan Singh · Bin Yu
Oral
Wed Jul 20 02:05 PM -- 02:25 PM (PDT) @ Room 307
The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation
In SA: Privacy-preserving Statistics and Machine Learning
Wei-Ning Chen · Ayfer Ozgur · Peter Kairouz
Oral
Wed Jul 20 02:05 PM -- 02:25 PM (PDT) @ Hall F
Re-evaluating Word Mover's Distance
In Applications
Ryoma Sato · Makoto Yamada · Hisashi Kashima
Oral
Wed Jul 20 02:05 PM -- 02:25 PM (PDT) @ Hall G
Causal Dynamics Learning for Task-Independent State Abstraction
In Reinforcement Learning
Zizhao Wang · Xuesu Xiao · Zifan Xu · Yuke Zhu · Peter Stone
Oral
Wed Jul 20 02:05 PM -- 02:25 PM (PDT) @ Ballroom 1 & 2
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
In Deep Learning
Haotao Wang · Aston Zhang · Yi Zhu · Shuai Zheng · Mu Li · Alex Smola · Zhangyang “Atlas” Wang
Oral
Wed Jul 20 02:05 PM -- 02:25 PM (PDT) @ Ballroom 3 & 4
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression
In Theory
Jingfeng Wu · Difan Zou · Vladimir Braverman · Quanquan Gu · Sham Kakade
Oral
Wed Jul 20 02:05 PM -- 02:25 PM (PDT) @ Room 309
Not All Poisons are Created Equal: Robust Training against Data Poisoning
In Deep Learning/Optimization
Yu Yang · Tian Yu Liu · Baharan Mirzasoleiman
Oral
Wed Jul 20 02:05 PM -- 02:25 PM (PDT) @ Room 301 - 303
Adversarially trained neural representations are already as robust as biological neural representations
In DL: Algorithms
Chong Guo · Michael Lee · Guillaume Leclerc · Joel Dapello · Yug Rao · Aleksander Madry · James DiCarlo
Oral
Wed Jul 20 02:05 PM -- 02:25 PM (PDT) @ Room 318 - 320
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
In Miscellaneous Aspects of Machine Learning/Reinforcement Learning
Piotr Tempczyk · Rafał Michaluk · Łukasz Garncarek · Przemysław Spurek · Jacek Tabor · Adam Golinski
Oral
Thu Jul 21 07:30 AM -- 07:50 AM (PDT) @ Hall G None
The Importance of Non-Markovianity in Maximum State Entropy Exploration
In Reinforcement Learning
Mirco Mutti · Riccardo De Santi · Marcello Restelli
Oral
Thu Jul 21 07:30 AM -- 07:50 AM (PDT) @ Ballroom 3 & 4
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
In T: Bandits/Online Learning/Reinforcement Learning
Andrew Wagenmaker · Yifang Chen · Max Simchowitz · Simon Du · Kevin Jamieson
Oral
Thu Jul 21 07:30 AM -- 07:50 AM (PDT) @ Room 307
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
In Theory/Social Aspects
sajad khodadadian · PRANAY SHARMA · Gauri Joshi · Siva Maguluri
Oral
Thu Jul 21 07:55 AM -- 08:15 AM (PDT) @ Room 309
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
In Miscellaneous Aspects of Machine Learning
Paul Rolland · Volkan Cevher · Matthäus Kleindessner · Chris Russell · Dominik Janzing · Bernhard Schölkopf · Francesco Locatello
Oral
Thu Jul 21 08:00 AM -- 08:20 AM (PDT) @ Room 318 - 320 None
G-Mixup: Graph Data Augmentation for Graph Classification
In Deep Learning/Optimization
Xiaotian Han · Zhimeng Jiang · Ninghao Liu · Xia Hu
Oral
Thu Jul 21 08:00 AM -- 08:20 AM (PDT) @ Room 327 - 329
Random Gegenbauer Features for Scalable Kernel Methods
In MISC/Deep Learning
Insu Han · Amir Zandieh · Haim Avron
Oral
Thu Jul 21 08:00 AM -- 08:20 AM (PDT) @ Ballroom 1 & 2
Overcoming Oscillations in Quantization-Aware Training
In Deep Learning
Markus Nagel · Marios Fournarakis · Yelysei Bondarenko · Tijmen Blankevoort
Oral
Thu Jul 21 08:05 AM -- 08:25 AM (PDT) @ Room 310
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
In Optimization/Reinforcement Learning
Simone Parisi · Aravind Rajeswaran · Senthil Purushwalkam · Abhinav Gupta
Oral
Thu Jul 21 08:05 AM -- 08:25 AM (PDT) @ Hall F
ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias
In APP: Physics/Computer Vision
Yupu Lu · Shijie Lin · Guanqi Chen · Jia Pan
Oral
Thu Jul 21 08:05 AM -- 08:25 AM (PDT) @ Room 301 - 303
It’s Raw! Audio Generation with State-Space Models
In DL: Generative Models and Autoencoders
Karan Goel · Albert Gu · Chris Donahue · Christopher Re
Oral
Thu Jul 21 08:10 AM -- 08:30 AM (PDT) @ Ballroom 3 & 4
Label Ranking through Nonparametric Regression
In T: Bandits/Online Learning/Reinforcement Learning
Dimitris Fotakis · Alkis Kalavasis · Eleni Psaroudaki
Oral
Thu Jul 21 08:15 AM -- 08:35 AM (PDT) @ Hall G
Causal Imitation Learning under Temporally Correlated Noise
In Reinforcement Learning
Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu
Oral
Thu Jul 21 08:15 AM -- 08:35 AM (PDT) @ Room 307
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming
In Theory/Social Aspects
Gregory Dexter · Agniva Chowdhury · Haim Avron · Petros Drineas
Oral
Thu Jul 21 10:30 AM -- 10:50 AM (PDT) @ Room 301 - 303
Learning Bellman Complete Representations for Offline Policy Evaluation
In Reinforcement Learning
Jonathan Chang · Kaiwen Wang · Nathan Kallus · Wen Sun
Oral
Thu Jul 21 10:30 AM -- 10:50 AM (PDT) @ Ballroom 3 & 4
UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees
In Theory: Game Theory and Optimization
Kimon Antonakopoulos · Dong Quan Vu · Volkan Cevher · Kfir Levy · Panayotis Mertikopoulos
Oral
Thu Jul 21 10:30 AM -- 10:50 AM (PDT) @ Room 327 - 329
Generalised Policy Improvement with Geometric Policy Composition
In Reinforcement Learning
Shantanu Thakoor · Mark Rowland · Diana Borsa · Will Dabney · Remi Munos · Andre Barreto
Oral
Thu Jul 21 10:30 AM -- 10:50 AM (PDT) @ Room 318 - 320
Streaming Algorithm for Monotone k-Submodular Maximization with Cardinality Constraints
In Optimization/Reinforcement Learning
Alina Ene · Huy Nguyen
Oral
Thu Jul 21 10:30 AM -- 10:50 AM (PDT) @ Room 310
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
In Probabilistic Methods/MISC
Konstantinos Kalais · Sotirios Chatzis
Oral
Thu Jul 21 11:00 AM -- 11:20 AM (PDT) @ Room 309
Sublinear-Time Clustering Oracle for Signed Graphs
In Deep Learning/MISC
Stefan Neumann · Pan Peng
Oral
Thu Jul 21 11:05 AM -- 11:25 AM (PDT) @ Room 307 None
Causal Conceptions of Fairness and their Consequences
In MISC/Social Aspects
Hamed Nilforoshan · Johann Gaebler · Ravi Shroff · Sharad Goel
Oral
Thu Jul 21 11:05 AM -- 11:25 AM (PDT) @ Ballroom 1 & 2
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
In Deep Learning: SSL/GNN
Kendrick Shen · Robbie Jones · Ananya Kumar · Sang Michael Xie · Jeff Z. HaoChen · Tengyu Ma · Percy Liang
Oral
Thu Jul 21 11:05 AM -- 11:25 AM (PDT) @ Hall F
Toward Compositional Generalization in Object-Oriented World Modeling
In Deep Learning: Attention Mechanisms
Linfeng Zhao · Lingzhi Kong · Robin Walters · Lawson Wong
Oral
Thu Jul 21 11:05 AM -- 11:25 AM (PDT) @ Hall G None
Do Differentiable Simulators Give Better Policy Gradients?
In Applications
Hyung Ju Suh · Max Simchowitz · Kaiqing Zhang · Russ Tedrake
Oral
Thu Jul 21 11:10 AM -- 11:30 AM (PDT) @ Room 310 None
Bayesian Model Selection, the Marginal Likelihood, and Generalization
In Probabilistic Methods/MISC
Sanae Lotfi · Pavel Izmailov · Gregory Benton · Micah Goldblum · Andrew Wilson
Oral
Thu Jul 21 11:10 AM -- 11:30 AM (PDT) @ Room 318 - 320
Deletion Robust Submodular Maximization over Matroids
In Optimization/Reinforcement Learning
PAUL DUETTING · Federico Fusco · Silvio Lattanzi · Ashkan Norouzi-Fard · Morteza Zadimoghaddam
Oral
Thu Jul 21 11:15 AM -- 11:35 AM (PDT) @ Room 301 - 303 None
Adversarially Trained Actor Critic for Offline Reinforcement Learning
In Reinforcement Learning
Ching-An Cheng · Tengyang Xie · Nan Jiang · Alekh Agarwal
Oral
Thu Jul 21 11:15 AM -- 11:35 AM (PDT) @ Room 327 - 329
Large Batch Experience Replay
In Reinforcement Learning
Thibault Lahire · Matthieu Geist · Emmanuel Rachelson
Oral
Thu Jul 21 11:15 AM -- 11:35 AM (PDT) @ Ballroom 3 & 4
A Convergent and Dimension-Independent Min-Max Optimization Algorithm
In Theory: Game Theory and Optimization
Vijay Keswani · Oren Mangoubi · Sushant Sachdeva · Nisheeth K. Vishnoi
Oral
Thu Jul 21 12:30 PM -- 12:50 PM (PDT) @ Room 309
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
In Reinforcement Learning/Optimization
Daniil Tiapkin · Denis Belomestny · Eric Moulines · Alexey Naumov · Sergey Samsonov · Yunhao Tang · Michal Valko · Pierre Menard
Oral
Thu Jul 21 12:30 PM -- 12:50 PM (PDT) @ Room 318 - 320
Generalized Strategic Classification and the Case of Aligned Incentives
In Social Aspects/Optimization
Sagi Levanon · Nir Rosenfeld
Oral
Thu Jul 21 01:00 PM -- 01:20 PM (PDT) @ Room 310
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
In Optimization/Probabilistic Methods
Giuseppe Bruno De Luca · Eva Silverstein
Oral
Thu Jul 21 01:00 PM -- 01:20 PM (PDT) @ Room 327 - 329
Anarchic Federated Learning
In Optimization/Theory
Haibo Yang · Xin Zhang · Prashant Khanduri · Jia Liu
Oral
Thu Jul 21 01:00 PM -- 01:20 PM (PDT) @ Hall G
Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
In Applications/Optimization
Martin Genzel · Ingo Gühring · Jan Macdonald · Maximilian März
Oral
Thu Jul 21 01:05 PM -- 01:25 PM (PDT) @ Ballroom 1 & 2
Measuring Representational Robustness of Neural Networks Through Shared Invariances
In Deep Learning
Vedant Nanda · Till Speicher · Camila Kolling · John P Dickerson · Krishna Gummadi · Adrian Weller
Oral
Thu Jul 21 01:05 PM -- 01:25 PM (PDT) @ Room 301 - 303
UniRank: Unimodal Bandit Algorithms for Online Ranking
In Applications/MISC
Camille-Sovanneary GAUTHIER · Romaric Gaudel · Elisa Fromont
Oral
Thu Jul 21 01:05 PM -- 01:25 PM (PDT) @ Ballroom 3 & 4
A Simple yet Universal Strategy for Online Convex Optimization
In T: Online Learning and Bandits
Lijun Zhang · Guanghui Wang · Jinfeng Yi · Tianbao Yang
Oral
Thu Jul 21 01:05 PM -- 01:25 PM (PDT) @ Room 307
An Analytical Update Rule for General Policy Optimization
In Optimization/Reinforcement Learning
Hepeng Li · Nicholas Clavette · Haibo He
Oral
Thu Jul 21 01:05 PM -- 01:25 PM (PDT) @ Hall F
Do More Negative Samples Necessarily Hurt In Contrastive Learning?
In Deep Learning
Pranjal Awasthi · Nishanth Dikkala · Pritish Kamath
Oral
Thu Jul 21 01:10 PM -- 01:30 PM (PDT) @ Room 318 - 320
Optimal Algorithms for Mean Estimation under Local Differential Privacy
In Social Aspects/Optimization
Hilal Asi · Vitaly Feldman · Kunal Talwar
Oral
Thu Jul 21 01:15 PM -- 01:35 PM (PDT) @ Room 309
Online Decision Transformer
In Reinforcement Learning/Optimization
Qinqing Zheng · Amy Zhang · Aditya Grover