ICML 2023 Tuesday 07/25

Timezone: Pacific/Honolulu

Full Schedule Sun 7/23 Mon 7/24 Tue 7/25 Wed 7/26 Thu 7/27 Fri 7/28 Sat 7/29

Registration Desk

Taking the Pulse Of Ethical ML in Health

Marzyeh Ghassemi

9:15 AM - 10:30 AM

Machine learning in health has made impressive progress in recent years, powered by an increasing availability of health-related data and high-capacity models. While many models in health now perform at, or above, humans in a range of tasks across the human lifespan, models also learn societal biases and may replicate or expand them. In this talk, Dr. Marzyeh Ghassemi will focus on the need for machine learning researchers and model developers to create robust models that can be ethically deployed in health settings, and beyond. Dr. Ghassemi's talk will span issues in data collection, outcome definition, algorithm development, and deployment considerations.

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Speaker Bio

Dr. Marzyeh Ghassemi is an Assistant Professor at MIT in Electrical Engineering and Computer Science (EECS) and Institute for Medical Engineering & Science (IMES), and a Vector Institute faculty member holding a Canadian CIFAR AI Chair and Canada Research Chair. She holds MIT affiliations with the Jameel Clinic and CSAIL. Professor Ghassemi holds a Herman L. F. von Helmholtz Career Development Professorship, and was named a CIFAR Azrieli Global Scholar and one of MIT Tech Review’s 35 Innovators Under 35. Previously, she was a Visiting Researcher with Alphabet’s Verily. She is currently on leave from the University of Toronto Departments of Computer Science and Medicine. Prior to her PhD in Computer Science at MIT, she received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar, and B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University.

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Poster

Poster Session 1

11:00 AM - 12:30 PM

300 Events in this session

POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models

Korawat Tanwisuth · Shujian Zhang · Huangjie Zheng · Pengcheng He · Mingyuan Zhou

Faith-Shap: The Faithful Shapley Interaction Index

Che-Ping Tsai · Chih-Kuan Yeh · Pradeep Ravikumar

Learning to Learn from APIs: Black-Box Data-Free Meta-Learning

Zixuan Hu · Li Shen · Zhenyi Wang · Baoyuan Wu · Chun Yuan · Dacheng Tao

Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization

Jiwoo Son · Minsu Kim · Hyeonah Kim · Jinkyoo Park

Learning Compiler Pass Orders using Coreset and Normalized Value Prediction

Youwei Liang · Kevin Stone · Ali Shameli · Chris Cummins · Mostafa Elhoushi · Jiadong Guo · Benoit Steiner · Xiaomeng Yang · Pengtao Xie · Hugh Leather · Yuandong Tian

TabLeak: Tabular Data Leakage in Federated Learning

Mark Vero · Mislav Balunovic · Dimitar I. Dimitrov · Martin Vechev

Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories

Qinqing Zheng · Mikael Henaff · Brandon Amos · Aditya Grover

Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language

Philipp Seidl · Andreu Vall · Sepp Hochreiter · Günter Klambauer

Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection

XiaoHui Zhang · Jiangyan Yi · Jianhua Tao · Chenglong Wang · Chu Yuan Zhang

Improving Fair Training under Correlation Shifts

Yuji Roh · Kangwook Lee · Steven Whang · Changho Suh

Phase-aware Adversarial Defense for Improving Adversarial Robustness

Dawei Zhou · Nannan Wang · Heng Yang · Xinbo Gao · Tongliang Liu

Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting

Shayan Shirahmad Gale Bagi · Zahra Gharaee · Oliver Schulte · Mark Crowley

Unlocking Slot Attention by Changing Optimal Transport Costs

Yan Zhang · David Zhang · Simon Lacoste-Julien · Gertjan Burghouts · Cees Snoek

Optimizing Hyperparameters with Conformal Quantile Regression

David Salinas · Jacek Golebiowski · Aaron Klein · Matthias Seeger · Cedric Archambeau

Sample and Predict Your Latent: Modality-free Sequential Disentanglement via Contrastive Estimation

Ilan Naiman · Nimrod Berman · Omri Azencot

Test-time Adaptation with Slot-Centric Models

Mihir Prabhudesai · Anirudh Goyal · Sujoy Paul · Sjoerd van Steenkiste · Mehdi S. M. Sajjadi · Gaurav Aggarwal · Thomas Kipf · Deepak Pathak · Katerina Fragkiadaki

Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth Labels

Min-Kook Suh · Seung-Woo Seo

Cooperative Open-ended Learning Framework for Zero-Shot Coordination

Yang Li · Shao Zhang · Jichen Sun · Yali Du · Ying Wen · Xinbing Wang · Wei Pan

Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning

Yihao Sun · Jiaji Zhang · Chengxing Jia · Haoxin Lin · Junyin Ye · Yang Yu

Eventual Discounting Temporal Logic Counterfactual Experience Replay

Cameron Voloshin · Abhinav Verma · Yisong Yue

PAC Generalization via Invariant Representations

Advait Parulekar · Karthikeyan Shanmugam · Sanjay Shakkottai

Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows

Chao Du · Tianbo Li · Tianyu Pang · Shuicheng YAN · Min Lin

Few-Sample Feature Selection via Feature Manifold Learning

David Cohen · Tal Shnitzer · Yuval Kluger · Ronen Talmon

Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation

Yu Chen · Wei Deng · Shikai Fang · Fengpei Li · Tianjiao N Yang · Yikai Zhang · Kashif Rasul · Shandian Zhe · Anderson Schneider · Yuriy Nevmyvaka

Why does Throwing Away Data Improve Worst-Group Error?

Kamalika Chaudhuri · Kartik Ahuja · Martin Arjovsky · David Lopez-Paz

TabDDPM: Modelling Tabular Data with Diffusion Models

Akim Kotelnikov · Dmitry Baranchuk · Ivan Rubachev · Artem Babenko

A Closer Look at the Intervention Procedure of Concept Bottleneck Models

Sungbin Shin · Yohan Jo · Sungsoo Ahn · Namhoon Lee

On Investigating the Conservative Property of Score-Based Generative Models

Chen-Hao Chao · Wei-Fang Sun · Bo-Wun Cheng · Chun-Yi Lee

Estimating Causal Effects using a Multi-task Deep Ensemble

Ziyang Jiang · Zhuoran Hou · Yiling Liu · Yiman Ren · Keyu Li · David Carlson

Differentiable and Transportable Structure Learning

Jeroen Berrevoets · Nabeel Seedat · Fergus Imrie · Mihaela van der Schaar

Monotonic Location Attention for Length Generalization

Jishnu Ray Chowdhury · Cornelia Caragea

Cramming: Training a Language Model on a single GPU in one day.

Jonas Geiping · Tom Goldstein

Federated Conformal Predictors for Distributed Uncertainty Quantification

Charles Lu · Yaodong Yu · Sai Praneeth Karimireddy · Michael Jordan · Ramesh Raskar

From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning

Edwige Cyffers · Aurélien Bellet · Debabrota Basu

Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning

Dohyun Kwon · Hanbaek Lyu

A Model-Based Method for Minimizing CVaR and Beyond

Si Yi Meng · Robert Gower

Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients

Marc Harkonen · Markus Lange-Hegermann · Bogdan Raita

Rotation and Translation Invariant Representation Learning with Implicit Neural Representations

Sehyun Kwon · Joo Young Choi · Ernest Ryu

Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models

Matthew Muckley · Alaaeldin El-Nouby · Karen Ullrich · Herve Jegou · Jakob Verbeek

Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding

Kenton Lee · Mandar Joshi · Iulia Turc · Hexiang Hu · Fangyu Liu · Julian M Eisenschlos · Urvashi Khandelwal · Peter Shaw · Ming-Wei Chang · Kristina Toutanova

Adaptive Coordination in Social Embodied Rearrangement

Andrew Szot · Unnat Jain · Dhruv Batra · Zsolt Kira · Ruta Desai · Akshara Rai

Active Policy Improvement from Multiple Black-box Oracles

Xuefeng Liu · Takuma Yoneda · Chaoqi Wang · Matthew Walter · Yuxin Chen

Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference

Yan Xu · Deqian Kong · Dehong Xu · Ziwei Ji · Bo Pang · Pascale FUNG · Ying Nian Wu

Robust Collaborative Learning with Linear Gradient Overhead

Sadegh Farhadkhani · Rachid Guerraoui · Nirupam Gupta · Lê-Nguyên Hoang · Rafael Pinot · John Stephan

Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data

Hien Dang · Tho Tran Huu · Stanley Osher · Hung Tran-The · Nhat Ho · TAN NGUYEN

On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation

Maohao Shen · Yuheng Bu · Gregory Wornell

Reprogramming Pretrained Language Models for Antibody Sequence Infilling

Igor Melnyk · Vijil Chenthamarakshan · Pin-Yu Chen · Payel Das · Amit Dhurandhar · Inkit Padhi · Devleena Das

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization

Jung Hyun Lee · Jeonghoon Kim · Se Jung Kwon · Dongsoo Lee

The Wisdom of Hindsight Makes Language Models Better Instruction Followers

Tianjun Zhang · Fangchen Liu · Justin Wong · Pieter Abbeel · Joseph E Gonzalez

Deep Regression Unlearning

Ayush Tarun · Vikram Chundawat · Murari Mandal · Mohan Kankanhalli

Bit Allocation using Optimization

Tongda Xu · Han Gao · Chenjian Gao · Yuanyuan Wang · Dailan He · Jinyong Pi · Jixiang Luo · Ziyu Zhu · Mao Ye · Hongwei Qin · Yan Wang · Jingjing Liu · Ya-Qin Zhang

QuantumDARTS: Differentiable Quantum Architecture Search for Variational Quantum Algorithms

Wenjie Wu · Ge Yan · Xudong Lu · Kaisen Pan · Junchi Yan

Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows

Phillip Si · Zeyi Chen · Subham S Sahoo · Yair Schiff · Volodymyr Kuleshov

DRew: Dynamically Rewired Message Passing with Delay

Benjamin Gutteridge · Xiaowen Dong · Michael Bronstein · Francesco Di Giovanni

Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction

Georgii Novikov · Daniel Bershatsky · Julia Gusak · Alex Shonenkov · Denis Dimitrov · Ivan Oseledets

Understanding Int4 Quantization for Language Models: Latency Speedup, Composability, and Failure Cases

Xiaoxia Wu · Cheng Li · Reza Yazdani Aminabadi · Zhewei Yao · Yuxiong He

SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient

Max Ryabinin · Tim Dettmers · Michael Diskin · Alexander Borzunov

Efficient Online Reinforcement Learning with Offline Data

Philip Ball · Laura Smith · Ilya Kostrikov · Sergey Levine

Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning

Mattia Atzeni · Mrinmaya Sachan · Andreas Loukas

Locally Regularized Neural Differential Equations: Some Black Boxes were meant to remain closed!

Avik Pal · Alan Edelman · Christopher Rackauckas

The Edge of Orthogonality: A Simple View of What Makes BYOL Tick

Pierre Richemond · Allison Tam · Yunhao Tang · Florian Strub · Bilal Piot · Feilx Hill

Second-order regression models exhibit progressive sharpening to the edge of stability

Atish Agarwala · Fabian Pedregosa · Jeffrey Pennington

Graph Neural Networks with Learnable and Optimal Polynomial Bases

Yuhe Guo · Zhewei Wei

SE(3) diffusion model with application to protein backbone generation

Jason Yim · Brian Trippe · Valentin De Bortoli · Emile Mathieu · Arnaud Doucet · Regina Barzilay · Tommi Jaakkola

Predicting Ordinary Differential Equations with Transformers

Sören Becker · Michal Klein · Alexander Neitz · Giambattista Parascandolo · Niki Kilbertus

Universal Morphology Control via Contextual Modulation

Zheng Xiong · Jacob Beck · Shimon Whiteson

Interactive Object Placement with Reinforcement Learning

Shengping Zhang · Quanling Meng · Qinglin Liu · Liqiang Nie · Bineng Zhong · Xiaopeng Fan · Rongrong Ji

GNOT: A General Neural Operator Transformer for Operator Learning

Zhongkai Hao · Zhengyi Wang · Hang Su · Chengyang Ying · Yinpeng Dong · LIU SONGMING · Ze Cheng · Jian Song · Jun Zhu

Local Vertex Colouring Graph Neural Networks

Shouheng Li · Dongwoo Kim · Qing Wang

Transformers Meet Directed Graphs

Simon Markus Geisler · Yujia Li · Daniel Mankowitz · Taylan Cemgil · Stephan Günnemann · Cosmin Paduraru

NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion

Jiatao Gu · Alex Trevithick · Kai-En Lin · Joshua M Susskind · Christian Theobalt · Lingjie Liu · Ravi Ramamoorthi

Under-Counted Tensor Completion with Neural Incorporation of Attributes

Shahana Ibrahim · Xiao Fu · Rebecca Hutchinson · Eugene Seo

Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning

Dingyang Chen · Qi Zhang

Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees

Pengfei Li · Jianyi Yang · Shaolei Ren

Smart Initial Basis Selection for Linear Programs

Zhenan Fan · Xinglu Wang · Oleksandr Yakovenko · Abdullah Ali Sivas · Owen Ren · Yong Zhang · Zirui Zhou

Conditional Graph Information Bottleneck for Molecular Relational Learning

Namkyeong Lee · Dongmin Hyun · Gyoung S. Na · Sungwon Kim · Junseok Lee · Chanyoung Park

Discover and Cure: Concept-aware Mitigation of Spurious Correlation

Shirley Wu · Mert Yuksekgonul · Linjun Zhang · James Zou

Reconstructive Neuron Pruning for Backdoor Defense

Yige Li · XIXIANG LYU · Xingjun Ma · Nodens Koren · Lingjuan Lyu · Bo Li · Yu-Gang Jiang

ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation

Kaiwen Zhou · Kaizhi Zheng · Connor Pryor · Yilin Shen · Hongxia Jin · Lise Getoor · Xin Eric Wang

Why do Nearest Neighbor Language Models Work?

Frank Xu · Uri Alon · Graham Neubig

Retrieval-Augmented Multimodal Language Modeling

Michihiro Yasunaga · Armen Aghajanyan · Weijia Shi · Richard James · Jure Leskovec · Percy Liang · Mike Lewis · Luke Zettlemoyer · Scott Yih

Optimizing Mode Connectivity for Class Incremental Learning

Haitao Wen · haoyang cheng · Heqian Qiu · Lanxiao Wang · Lili Pan · Hongliang Li

Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models

Luke Vilnis · Yury Zemlyanskiy · Patrick Murray · Alexandre Passos · Sumit Sanghai

Variational Autoencoding Neural Operators

Jacob H. Seidman · Georgios Kissas · George J. Pappas · Paris Perdikaris

TAN Without a Burn: Scaling Laws of DP-SGD

Tom Sander · Pierre Stock · Alexandre Sablayrolles

A Coupled Flow Approach to Imitation Learning

Gideon Freund · Elad Sarafian · Sarit Kraus

On Kinetic Optimal Probability Paths for Generative Models

Neta Shaul · Ricky T. Q. Chen · Maximilian Nickel · Matthew Le · Yaron Lipman

Bayesian Design Principles for Frequentist Sequential Learning

Yunbei Xu · Assaf Zeevi

Speeding Up Bellman Ford via Minimum Violation Permutations

Silvio Lattanzi · Ola Svensson · Sergei Vassilvitskii

On User-Level Private Convex Optimization

Badih Ghazi · Pritish Kamath · Ravi Kumar · Pasin Manurangsi · Raghu Meka · Chiyuan Zhang

Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback

Tal Lancewicki · Aviv Rosenberg · Dmitry Sotnikov

FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks

Bingqing Song · Prashant Khanduri · xinwei zhang · Jinfeng Yi · Mingyi Hong

Contrastive Learning Meets Homophily: Two Birds with One Stone

Dongxiao He · JiTao Zhao · Rui Guo · Zhiyong Feng · Di Jin · Yuxiao Huang · Zhen Wang · Weixiong Zhang

mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video

Haiyang Xu · Qinghao Ye · Ming Yan · Yaya Shi · Jiabo Ye · yuanhong xu · Chenliang Li · Bin Bi · Qi Qian · Wei Wang · Guohai Xu · Ji Zhang · Songfang Huang · Fei Huang · Jingren Zhou

ODS: Test-Time Adaptation in the Presence of Open-World Data Shift

Zhi Zhou · Lan-Zhe Guo · Lin-Han Jia · Dingchu Zhang · Yu-Feng Li

Fractional Denoising for 3D Molecular Pre-training

Shikun Feng · Yuyan Ni · Yanyan Lan · Zhiming Ma · Wei-Ying Ma

Self-Interpretable Time Series Prediction with Counterfactual Explanations

Jingquan Yan · Hao Wang

Prompting Large Language Model for Machine Translation: A Case Study

Biao Zhang · Barry Haddow · Alexandra Birch

Uncertain Evidence in Probabilistic Models and Stochastic Simulators

Andreas Munk · Alexander Mead · Frank Wood

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting

Hilaf Hasson · Danielle Robinson · Yuyang Wang · Gaurav Gupta · Youngsuk Park

Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk

David Simchi-Levi · Chonghuan Wang

Tight and fast generalization error bound of graph embedding in metric space

Atsushi Suzuki · Atsushi Nitanda · Taiji Suzuki · Jing Wang · Feng Tian · Kenji Yamanishi

Extrapolated Random Tree for Regression

Yuchao Cai · Yuheng Ma · Yiwei Dong · Hanfang Yang

Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes

Runlong Zhou · Ruosong Wang · Simon Du

Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes

Chenlu Ye · Wei Xiong · Quanquan Gu · Tong Zhang

Active causal structure learning with advice

Davin Choo · Themis Gouleakis · Arnab Bhattacharyya

Active Ranking of Experts Based on their Performances in Many Tasks

El Mehdi Saad · Nicolas Verzelen · Alexandra Carpentier

Sequential Changepoint Detection via Backward Confidence Sequences

Shubhanshu Shekhar · Aaditya Ramdas

Quantum Lower Bounds for Finding Stationary Points of Nonconvex Functions

Chenyi Zhang · Tongyang Li

The Monge Gap: A Regularizer to Learn All Transport Maps

Théo Uscidda · Marco Cuturi

Learning Distributions over Quantum Measurement Outcomes

Weiyuan Gong · Scott Aaronson

Approximation Algorithms for Fair Range Clustering

Sedjro Salomon Hotegni · Sepideh Mahabadi · Ali Vakilian

Short-lived High-volume Bandits

Su Jia · Nishant Oli · Ian Anderson · Paul Duff · Andrew Li · R. Ravi

Optimal randomized multilevel Monte Carlo for repeatedly nested expectations

Yasa Syed · Guanyang Wang

Minimax estimation of discontinuous optimal transport maps: The semi-discrete case

Aram-Alexandre Pooladian · Vincent Divol · Jonathan Niles-Weed

Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function

Elissa Mhanna · Mohamad Assaad

Global Selection of Contrastive Batches via Optimization on Sample Permutations

Vin Sachidananda · Ziyi Yang · Chenguang Zhu

Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability

Thomy Phan · Fabian Ritz · Philipp Altmann · Maximilian Zorn · Jonas Nüßlein · Michael Kölle · Thomas Gabor · Claudia Linnhoff-Popien

Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series

Abdul Fatir Ansari · Alvin Heng · Andre Lim · Harold Soh

Topologically Faithful Image Segmentation via Induced Matching of Persistence Barcodes

Nico Stucki · Johannes C. Paetzold · Suprosanna Shit · bjoern menze · Ulrich Bauer

MetricGAN-OKD: Multi-Metric Optimization of MetricGAN via Online Knowledge Distillation for Speech Enhancement

Wooseok Shin · Byung Hoon Lee · Jin Sob Kim · Hyun Joon Park · Sung Won Han

FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models

Songze Li · Duanyi YAO · Jin Liu

A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening

Yifan Chen · Rentian Yao · Yun Yang · Jie Chen

Dataset Distillation with Convexified Implicit Gradients

Noel Loo · Ramin Hasani · Mathias Lechner · Daniela Rus

Fast Federated Machine Unlearning with Nonlinear Functional Theory

Tianshi Che · Yang Zhou · Zijie Zhang · Lingjuan Lyu · Ji Liu · Da Yan · Dejing Dou · Jun Huan

Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models

Nikolaos Dimitriadis · Pascal Frossard · François Fleuret

Boosting Offline Reinforcement Learning with Action Preference Query

Qisen Yang · Shenzhi Wang · Matthieu Lin · Shiji Song · Gao Huang

SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification

Pranjal Aggarwal · Ameet Deshpande · Karthik Narasimhan

Uncertainty Estimation for Molecules: Desiderata and Methods

Tom Wollschläger · Nicholas Gao · Bertrand Charpentier · Mohamed Amine Ketata · Stephan Günnemann

Reliable Measures of Spread in High Dimensional Latent Spaces

Anna Marbut · Katy McKinney-Bock · Travis Wheeler

Causal Strategic Classification: A Tale of Two Shifts

Guy Horowitz · Nir Rosenfeld

Learning Preconditioners for Conjugate Gradient PDE Solvers

Yichen Li · Peter Yichen Chen · Tao Du · Wojciech Matusik

Flexible Phase Dynamics for Bio-Plausible Contrastive Learning

Ezekiel Williams · Colin Bredenberg · Guillaume Lajoie

Learning Belief Representations for Partially Observable Deep RL

Andrew Wang · Andrew C Li · Toryn Q Klassen · Rodrigo A Toro Icarte · Sheila McIlraith

A Neural PDE Solver with Temporal Stencil Modeling

Zhiqing Sun · Yiming Yang · Shinjae Yoo

Towards Understanding Ensemble Distillation in Federated Learning

Sejun Park · Kihun Hong · Ganguk Hwang

Improved Active Multi-Task Representation Learning via Lasso

Yiping Wang · Yifang Chen · Kevin Jamieson · Simon Du

Bag of Tricks for Training Data Extraction from Language Models

Weichen Yu · Tianyu Pang · Qian Liu · Chao Du · Bingyi Kang · Yan Huang · Min Lin · Shuicheng YAN

GFlowNet-EM for Learning Compositional Latent Variable Models

Edward Hu · Nikolay Malkin · Moksh Jain · Katie Everett · Alexandros Graikos · Yoshua Bengio

Optimizing DDPM Sampling with Shortcut Fine-Tuning

Ying Fan · Kangwook Lee

The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms

Anirudh Vemula · Yuda Song · Aarti Singh · J. Bagnell · Sanjiban Choudhury

A Reinforcement Learning Framework for Dynamic Mediation Analysis

Lin Ge · Jitao Wang · Chengchun Shi · Zhenke Wu · Rui Song

Additive Causal Bandits with Unknown Graph

Alan Malek · Virginia Aglietti · Silvia Chiappa

VA-learning as a more efficient alternative to Q-learning

Yunhao Tang · Remi Munos · Mark Rowland · Michal Valko

MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation

Xingang Peng · Jiaqi Guan · qiang liu · Jianzhu Ma

Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling

Tianqi Chen · Mingyuan Zhou

Emergence of Sparse Representations from Noise

Trenton Bricken · Rylan Schaeffer · Bruno Olshausen · Gabriel Kreiman

The Benefits of Model-Based Generalization in Reinforcement Learning

Kenny Young · Aditya Ramesh · Louis Kirsch · Jürgen Schmidhuber

K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs

Andrea Coletta · Svitlana Vyetrenko · Tucker Balch

Inflow, Outflow, and Reciprocity in Machine Learning

Mukund Sundararajan · Walid Krichene

Infinite Action Contextual Bandits with Reusable Data Exhaust

Mark Rucker · Yinglun Zhu · Paul Mineiro

Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games

Dylan Foster · Noah Golowich · Sham Kakade

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions

Zhe Feng · Christopher Liaw · Zixin Zhou

Bandits with Knapsacks: Advice on Time-Varying Demands

Lixing Lyu · Wang Chi Cheung

A Kernel-Based View of Language Model Fine-Tuning

Sadhika Malladi · Alexander Wettig · Dingli Yu · Danqi Chen · Sanjeev Arora

Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies

Hannah Pinson · Joeri Lenaerts · Vincent Ginis

Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data Subpopulations

Weixin Liang · Yining Mao · Yongchan Kwon · Xinyu Yang · James Zou

Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels

Xuchen You · Shouvanik Chakrabarti · Boyang Chen · Xiaodi Wu

Slot-VAE: Object-Centric Scene Generation with Slot Attention

Yanbo Wang · Letao Liu · Justin Dauwels

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification

Nan Yin · Li Shen · Mengzhu Wang · Long Lan · Zeyu Ma · Chong Chen · Xian-Sheng Hua · Xiao Luo

Efficient Training of Language Models using Few-Shot Learning

Sashank Jakkam Reddi · Sobhan Miryoosefi · Stefani Karp · Shankar Krishnan · Satyen Kale · Seungyeon Kim · Sanjiv Kumar

Identifying Useful Learnwares for Heterogeneous Label Spaces

Lan-Zhe Guo · Zhi Zhou · Yu-Feng Li · Zhi-Hua Zhou

Adaptive Compositional Continual Meta-Learning

Bin Wu · Jinyuan Fang · xiangxiang Zeng · Shangsong Liang · Qiang Zhang

D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching

Xuanzhou Liu · Lin Zhang · Jiaqi Sun · Yujiu Yang · Haiqin Yang

What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings

Zequn Sun · Jiacheng Huang · Xiaozhou Xu · Qijin Chen · Weijun Ren · Wei Hu

FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization

Zhen WANG · Weirui Kuang · Ce Zhang · Bolin Ding · Yaliang Li

A theory of continuous generative flow networks

Salem Lahlou · Tristan Deleu · Pablo Lemos · Dinghuai Zhang · Alexandra Volokhova · Alex Hernandez-Garcia · Lena Nehale Ezzine · Yoshua Bengio · Nikolay Malkin

Neural signature kernels as infinite-width-depth-limits of controlled ResNets

Nicola Muca Cirone · Maud Lemercier · Cristopher Salvi

Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference

Kyurae Kim · Kaiwen Wu · Jisu Oh · Jacob Gardner

A Robust Test for the Stationarity Assumption in Sequential Decision Making

Jitao Wang · Chengchun Shi · Zhenke Wu

FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization

Hao Zhang · Chenglin Li · Wenrui Dai · Junni Zou · Hongkai Xiong

Auxiliary Learning as an Asymmetric Bargaining Game

Aviv Shamsian · Aviv Navon · Neta Glazer · Kenji Kawaguchi · Gal Chechik · Ethan Fetaya

Retrosynthetic Planning with Dual Value Networks

Guoqing Liu · Di Xue · Shufang Xie · Yingce Xia · Austin Tripp · Krzysztof Maziarz · Marwin Segler · Tao Qin · Zongzhang Zhang · Tie-Yan Liu

Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization

Cheuk Yin Lin · Chaobing Song · Jelena Diakonikolas

Generating Private Synthetic Data with Genetic Algorithms

Terrance Liu · Jingwu Tang · Giuseppe Vietri · Steven Wu

AdaBoost is not an Optimal Weak to Strong Learner

Mikael Møller Høgsgaard · Kasper Green Larsen · Martin Ritzert

Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation

Weijian Deng · Yumin Suh · Stephen Gould · Liang Zheng

Unsupervised Skill Discovery for Learning Shared Structures across Changing Environments

Sang-Hyun Lee · Seung-Woo Seo

Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics

Pingchuan Ma · Peter Yichen Chen · Bolei Deng · Josh Tenenbaum · Tao Du · Chuang Gan · Wojciech Matusik

Learning Neural PDE Solvers with Parameter-Guided Channel Attention

Makoto Takamoto · Francesco Alesiani · Mathias Niepert

SLAMB: Accelerated Large Batch Training with Sparse Communication

Hang Xu · Wenxuan Zhang · Jiawei Fei · Yuzhe Wu · TingWen Xie · Jun Huang · Yuchen Xie · Mohamed Elhoseiny · Panos Kalnis

GuardHFL: Privacy Guardian for Heterogeneous Federated Learning

Hanxiao Chen · Meng Hao · Hongwei Li · Kangjie Chen · Guowen Xu · Tianwei Zhang · Xilin Zhang

Continuation Path Learning for Homotopy Optimization

Xi Lin · Zhiyuan Yang · Xiaoyuan Zhang · Qingfu Zhang

NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes

Chenbo Jiang · Jie Yang · Shwai He · Yu-Kun Lai · Lin Gao

Margin-based Neural Network Watermarking

Byungjoo Kim · Suyoung Lee · Seanie Lee · Son · Sung Ju Hwang

Sequential Underspecified Instrument Selection for Cause-Effect Estimation

Elisabeth Ailer · Jason Hartford · Niki Kilbertus

JAWS-X: Addressing Efficiency Bottlenecks of Conformal Prediction Under Standard and Feedback Covariate Shift

Drew Prinster · Suchi Saria · Anqi Liu

Constrained Phi-Equilibria

Martino Bernasconi · Matteo Castiglioni · Alberto Marchesi · Francesco Trovò · Nicola Gatti

Diffusion Models are Minimax Optimal Distribution Estimators

Kazusato Oko · Shunta Akiyama · Taiji Suzuki

Statistical Indistinguishability of Learning Algorithms

Alkis Kalavasis · Amin Karbasi · Shay Moran · Grigoris Velegkas

Cluster Explanation via Polyhedral Descriptions

Connor Lawless · Oktay Gunluk

Geometric Clifford Algebra Networks

David Ruhe · Jayesh K. Gupta · Steven De Keninck · Max Welling · Johannes Brandstetter

Multisample Flow Matching: Straightening Flows with Minibatch Couplings

Aram-Alexandre Pooladian · Heli Ben-Hamu · Carles Domingo i Enrich · Brandon Amos · Yaron Lipman · Ricky T. Q. Chen

Memory-Based Dual Gaussian Processes for Sequential Learning

Paul Chang · Prakhar Verma · ST John · Arno Solin · Khan Emtiyaz

Training-Free Neural Active Learning with Initialization-Robustness Guarantees

Apivich Hemachandra · Zhongxiang Dai · Jasraj Singh · See-Kiong Ng · Bryan Kian Hsiang Low

Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents

WENHAO XU · Xuefeng Gao · Xuedong He

Correcting discount-factor mismatch in on-policy policy gradient methods

Fengdi Che · Gautham Vasan · Rupam Mahmood

A Fast Optimistic Method for Monotone Variational Inequalities

Michael Sedlmayer · Dang-Khoa Nguyen · Radu Ioan Bot

Doubly Adversarial Federated Bandits

Jialin Yi · Milan Vojnovic

Demystifying Disagreement-on-the-Line in High Dimensions

Donghwan Lee · Behrad Moniri · Xinmeng Huang · Edgar Dobriban · Hamed Hassani

ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval

Kexun Zhang · Xianjun Yang · William Wang · Lei Li

Unifying Molecular and Textual Representations via Multi-task Language Modelling

Dimitrios Christofidellis · Giorgio Giannone · Jannis Born · Ole Winther · Teodoro Laino · Matteo Manica

Evidential Interactive Learning for Medical Image Captioning

Ervine Zheng · Qi Yu

Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation

Antoine Dedieu · Guangyao Zhou · Dileep George · Miguel Lazaro-Gredilla

Hindsight Learning for MDPs with Exogenous Inputs

Sean R. Sinclair · Felipe Vieira Frujeri · Ching-An Cheng · Luke Marshall · Hugo Barbalho · Jingling Li · Jennifer Neville · Ishai Menache · Adith Swaminathan

Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples

Dongyoon Yang · Insung Kong · Yongdai Kim

Adversarially Robust PAC Learnability of Real-Valued Functions

Idan Attias · Steve Hanneke

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path

Qiwei Di · Jiafan He · Dongruo Zhou · Quanquan Gu

MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations

Anqi Li · Byron Boots · Ching-An Cheng

Truncating Trajectories in Monte Carlo Reinforcement Learning

Riccardo Poiani · Alberto Maria Metelli · Marcello Restelli

Approximately Optimal Core Shapes for Tensor Decompositions

Mehrdad Ghadiri · Matthew Fahrbach · Thomas Fu · Vahab Mirrokni

Data Structures for Density Estimation

Anders Aamand · Alexandr Andoni · Justin Chen · Piotr Indyk · Shyam Narayanan · Sandeep Silwal

Internet Explorer: Targeted Representation Learning on the Open Web

Alexander Li · Ellis Brown · Alexei Efros · Deepak Pathak

Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator

Sicheng Zhu · Bang An · Furong Huang · Sanghyun Hong

DRCFS: Doubly Robust Causal Feature Selection

Francesco Quinzan · Ashkan Soleymani · Patrick Jaillet · Cristian R. Rojas · Stefan Bauer

Polarity Is All You Need to Learn and Transfer Faster

Alice (Qingyang) Wang · Michael Powell · Eric Bridgeford · Ali Geisa · Joshua Vogelstein

Homomorphism AutoEncoder --- Learning Group Structured Representations from Observed Transitions

Hamza Keurti · Hsiao-Ru Pan · Michel Besserve · Benjamin F. Grewe · Bernhard Schölkopf

RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents

Rafael A Rodriguez-Sanchez · Benjamin Spiegel · Jennifer Wang · Roma Patel · Stefanie Tellex · George Konidaris

Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation

Ronghao Dang · Lu Chen · Liuyi Wang · Zongtao He · Chengju Liu · Qijun Chen

Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling

Arka Daw · Jie Bu · Sifan Wang · Paris Perdikaris · Anuj Karpatne

A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks

David Boetius · Stefan Leue · Tobias Sutter

Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models

Ajay Jaiswal · Shiwei Liu · Tianlong Chen · Ding · Zhangyang “Atlas” Wang

Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels

Sai Rajeswar · Pietro Mazzaglia · Tim Verbelen · Alex Piche · Bart Dhoedt · Aaron Courville · Alexandre Lacoste

Do Machine Learning Models Learn Statistical Rules Inferred from Data?

Aaditya Naik · Yinjun Wu · Mayur Naik · Eric Wong

Set-membership Belief State-based Reinforcement Learning for POMDPs

Wei Wei · Lijun Zhang · Lin Li · Huizhong Song · Jiye Liang

Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference

Insung Kong · Dongyoon Yang · Jongjin Lee · Ilsang Ohn · GYUSEUNG BAEK · Yongdai Kim

Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards

Yulian Wu · Xingyu Zhou · Sayak Ray Chowdhury · Di Wang

Online Learning with Feedback Graphs: The True Shape of Regret

Tomáš Kocák · Alexandra Carpentier

High Probability Convergence of Stochastic Gradient Methods

Zijian Liu · Ta Duy Nguyen · Thien Nguyen · Alina Ene · Huy Nguyen

On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits

Weitong Zhang · Jiafan He · Zhiyuan Fan · Quanquan Gu

Sequence Modeling with Multiresolution Convolutional Memory

Jiaxin Shi · Ke Alexander Wang · Emily Fox

Dropout Reduces Underfitting

Zhuang Liu · Zhiqiu (Oscar) Xu · Joseph Jin · Zhiqiang Shen · Trevor Darrell

Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis

Sanjay Kariyappa · Chuan Guo · Kiwan Maeng · Wenjie Xiong · G. Edward Suh · Moinuddin Qureshi · Hsien-Hsin Sean Lee

Uncovering Adversarial Risks of Test-Time Adaptation

Tong Wu · Feiran Jia · Xiangyu Qi · Jiachen Wang · Vikash Sehwag · Saeed Mahloujifar · Prateek Mittal

Trapdoor Normalization with Irreversible Ownership Verification

Hanwen Liu · Zhenyu Weng · Yuesheng Zhu · Yadong Mu

Detecting Out-of-distribution Data through In-distribution Class Prior

Xue JIANG · Feng Liu · zhen fang · Hong Chen · Tongliang Liu · Feng Zheng · Bo Han

Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning

Mingqi Yuan · Bo Li · Xin Jin · Wenjun Zeng

MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation

Omer Bar-Tal · Lior Yariv · Yaron Lipman · Tali Dekel

Unsupervised Out-of-Distribution Detection with Diffusion Inpainting

Zhenzhen Liu · Jin Zhou · Yufan Wang · Kilian Weinberger

Markovian Gaussian Process Variational Autoencoders

Harrison Zhu · Carles Balsells Rodas · Yingzhen Li

On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs

Richard Watson · Hengrui Cai · Xinming An · Samuel McLean · Rui Song

Convergence of Proximal Point and Extragradient-Based Methods Beyond Monotonicity: the Case of Negative Comonotonicity

Eduard Gorbunov · Adrien Taylor · Samuel Horváth · Gauthier Gidel

Large Language Models Struggle to Learn Long-Tail Knowledge

Nikhil Kandpal · Haikang Deng · Adam Roberts · Eric Wallace · Colin Raffel

Invariance in Policy Optimisation and Partial Identifiability in Reward Learning

Joar Skalse · Matthew Farrugia-Roberts · Stuart Russell · Alessandro Abate · Adam Gleave

Global optimality for Euclidean CCCP under Riemannian convexity

Melanie Weber · Suvrit Sra

Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice

Toshinori Kitamura · Tadashi Kozuno · Yunhao Tang · Nino Vieillard · Michal Valko · Wenhao Yang · Jincheng Mei · Pierre Menard · Mohammad Gheshlaghi Azar · Remi Munos · Olivier Pietquin · Matthieu Geist · Csaba Szepesvari · Wataru Kumagai · Yutaka Matsuo

On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs

Romain Laroche · Remi Tachet des Combes

Achieving High Accuracy with PINNs via Energy Natural Gradient Descent

Johannes Müller · Marius Zeinhofer

A Distribution Optimization Framework for Confidence Bounds of Risk Measures

Hao Liang · Zhi-Quan Luo

A Kernel Stein Test of Goodness of Fit for Sequential Models

Jerome Baum · Heishiro Kanagawa · Arthur Gretton

PCA-based Multi-Task Learning: a Random Matrix Approach

Malik TIOMOKO · Romain COUILLET · Frederic Pascal

Neural Status Registers

Lukas Faber · Roger Wattenhofer

Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network

Marie Guyomard · Susana Barbosa · Lionel Fillatre

Free-Form Variational Inference for Gaussian Process State-Space Models

Xuhui Fan · Edwin V. Bonilla · Terence O'kane · Scott SIsson

Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach

Boya Hou · Sina Sanjari · Nathan Dahlin · Subhonmesh Bose · Umesh Vaidya

Rethinking Backdoor Attacks

Alaa Khaddaj · Guillaume Leclerc · Aleksandar Makelov · Kristian Georgiev · Hadi Salman · Andrew Ilyas · Aleksander Madry

Do Perceptually Aligned Gradients Imply Robustness?

Roy Ganz · Bahjat Kawar · Michael Elad

Understanding Oversquashing in GNNs through the Lens of Effective Resistance

Mitchell Black · Zhengchao Wan · Amir Nayyeri · Yusu Wang

One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding

Daniel Severo · James Townsend · Ashish Khisti · Alireza Makhzani

Solving High-Dimensional PDEs with Latent Spectral Models

Haixu Wu · Tengge Hu · huakun luo · Jianmin Wang · Mingsheng Long

Two-Scale Gradient Descent Ascent Dynamics Finds Mixed Nash Equilibria of Continuous Games: A Mean-Field Perspective

Yulong Lu

An Information-Theoretic Analysis of Nonstationary Bandit Learning

Seungki Min · Daniel Russo

Learning Representations without Compositional Assumptions

Tennison Liu · Jeroen Berrevoets · Zhaozhi Qian · Mihaela van der Schaar

Meta-learning Parameterized Skills

Haotian Fu · Shangqun Yu · Saket Tiwari · Michael L. Littman · George Konidaris

On the Convergence Rate of Gaussianization with Random Rotations

Felix Draxler · Lars Kühmichel · Armand Rousselot · Jens Müller · Christoph Schnörr · Ullrich Koethe

Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs

Raif Rustamov · Subhabrata Majumdar

Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets

Yurong Chen · Qian Wang · Zhijian Duan · Haoran Sun · Zhaohua Chen · Xiang Yan · Xiaotie Deng

TGRL: An Algorithm for Teacher Guided Reinforcement Learning

Idan Shenfeld · Zhang-Wei Hong · Aviv Tamar · Pulkit Agrawal

The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing

Xingyu Xu · Yandi Shen · Yuejie Chi · Cong Ma

FeDXL: Provable Federated Learning for Deep X-Risk Optimization

Zhishuai Guo · Rong Jin · Jiebo Luo · Tianbao Yang

Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization

Lesi Chen · Jing Xu · Luo Luo

Efficient List-Decodable Regression using Batches

Abhimanyu Das · Ayush Jain · Weihao Kong · Rajat Sen

Approximate Stein Classes for Truncated Density Estimation

Daniel J. Williams · Song Liu

Rethinking Weak Supervision in Helping Contrastive Learning

Jingyi Cui · Weiran Huang · Yifei Wang · Yisen Wang

Spatial Implicit Neural Representations for Global-Scale Species Mapping

Elijah Cole · Grant Horn · Christian Lange · Alexander Shepard · Patrick Leary · Pietro Perona · Scott Loarie · Oisin Mac Aodha

Interventional Causal Representation Learning

Kartik Ahuja · Divyat Mahajan · Yixin Wang · Yoshua Bengio

Denoising MCMC for Accelerating Diffusion-Based Generative Models

Beomsu Kim · Jong Chul YE

Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

Gati Aher · Rosa I. Arriaga · Adam Tauman Kalai

Poisoning Generative Replay in Continual Learning to Promote Forgetting

Siteng Kang · Zhan Shi · Xinhua Zhang

Continual Task Allocation in Meta-Policy Network via Sparse Prompting

Yijun Yang · Tianyi Zhou · Jing Jiang · Guodong Long · Yuhui Shi

Text-To-Concept (and Back) via Cross-Model Alignment

Mazda Moayeri · Keivan Rezaei · Maziar Sanjabi · Soheil Feizi

Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution

Ruofan Zhang · Ruofan Zhang · Haoyu Chen · Chao Dong · Yulun Zhang · Wenming Yang

Maximum Optimality Margin: A Unified Approach for Contextual Linear Programming and Inverse Linear Programming

Chunlin Sun · Shang Liu · Xiaocheng Li

Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers

Yineng Chen · Zuchao Li · Lefei Zhang · Bo Du · hai zhao

State and parameter learning with PARIS particle Gibbs

Gabriel Cardoso · Yazid Janati el idrissi · Sylvain Le Corff · Eric Moulines · Jimmy Olsson

On Computing Optimal Tree Ensembles

Christian Komusiewicz · Pascal Kunz · Frank Sommer · Manuel Sorge

Opponent-Limited Online Search for Imperfect Information Games

Weiming Liu · Haobo Fu · Qiang Fu · Wei Yang

Robust Subtask Learning for Compositional Generalization

Kishor Jothimurugan · Steve Hsu · Osbert Bastani · Rajeev Alur

Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits

Ronshee Chawla · Daniel Vial · Sanjay Shakkottai · R Srikant

Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning

Antonio Sclocchi · Mario Geiger · Matthieu Wyart

Fully Dynamic Submodular Maximization over Matroids

PAUL DUETTING · Federico Fusco · Silvio Lattanzi · Ashkan Norouzi-Fard · Morteza Zadimoghaddam

One-sided Matrix Completion from Two Observations Per Row

Steven Cao · Percy Liang · Greg Valiant

The Saddle-Point Method in Differential Privacy

Wael Alghamdi · Felipe Gomez · Shahab Asoodeh · Flavio Calmon · Oliver Kosut · Lalitha Sankar

Geometric Autoencoders - What You See is What You Decode

Philipp Nazari · Sebastian Damrich · Fred Hamprecht

Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape

Yan Sun · Li Shen · Shixiang Chen · Liang Ding · Dacheng Tao

DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation

Yuhang Lai · Chengxi Li · Yiming Wang · Tianyi Zhang · Ruiqi Zhong · Luke Zettlemoyer · Scott Yih · Daniel Fried · Sida Wang · Tao Yu

Continual Learning in Linear Classification on Separable Data

Itay Evron · Edward Moroshko · Gon Buzaglo · Maroun Khriesh · Badea Marjieh · Nati Srebro · Daniel Soudry

The Implicit Regularization of Dynamical Stability in Stochastic Gradient Descent

Lei Wu · Weijie Su

Multi-channel Autobidding with Budget and ROI Constraints

Yuan Deng · Negin Golrezaei · Patrick Jaillet · Jason Cheuk Nam Liang · Vahab Mirrokni

Raising the Cost of Malicious AI-Powered Image Editing

Hadi Salman · Alaa Khaddaj · Guillaume Leclerc · Andrew Ilyas · Aleksander Madry

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Poster

Poster Session 2

2:00 PM - 3:30 PM

343 Events in this session

Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach

Yanwei Jia · Xun Yu Zhou

Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima

Brian Swenson · Ryan Murray · H. Vincent Poor · Soummya Kar

Weakly Supervised Disentangled Generative Causal Representation Learning

Xinwei Shen · Furui Liu · Hanze Dong · Qing Lian · Zhitang Chen · Tong Zhang

Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism

Yu-Guan Hsieh · Franck Iutzeler · Jérôme Malick · Panayotis Mertikopoulos

Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks

Aleksandr Shevchenko · Vyacheslav Kungurtsev · Marco Mondelli

Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials

Daiqi Gao · Yufeng Liu · Donglin Zeng

Data-Derived Weak Universal Consistency

Narayana Prasad Santhanam · Venkatachalam Anantharam · Wojciech Szpankowski

On the Convergence Rates of Policy Gradient Methods

Lin Xiao

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence

Julie Nutini · Issam Laradji · Mark Schmidt

Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks

El Mehdi Achour · Francois Malgouyres · Franck Mamalet

Constraint Reasoning Embedded Structured Prediction

Nan Jiang · Maosen Zhang · Willem-Jan van Hoeve · Yexiang Xue

Project and Forget: Solving Large-Scale Metric Constrained Problems

Rishi Sonthalia · Anna C. Gilbert

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms

Shengyi Huang · Rousslan Fernand Julien Dossa · Chang Ye · Jeff Braga · Dipam Chakraborty · Kinal Mehta · João Madeira Araujo

Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables

Yaniv Yacoby · Weiwei Pan · Finale Doshi-Velez

MALTS: Matching After Learning to Stretch

Harsh Parikh · Cynthia Rudin · Alexander Volfovsky

Exploiting locality in high-dimensional Factorial hidden Markov models

Lorenzo Rimella · Nick Whiteley

abess: A Fast Best-Subset Selection Library in Python and R

Jin Zhu · Xueqin Wang · Liyuan Hu · Junhao Huang · Kangkang Jiang · Yanhang Zhang · Shiyun Lin · Junxian Zhu

Adversarial Classification: Necessary Conditions and Geometric Flows

Nicolas Garcia Trillos · Ryan Murray

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Sarbojit Roy · Soham Sarkar · Subhajit Dutta · Anil K. Ghosh

Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable

Baoluo Sun · Yifan Cui · Eric Tchetgen Tchetgen

XAI Beyond Classification: Interpretable Neural Clustering

Xi Peng · Yunfan Li · Ivor W. Tsang · Hongyuan Zhu · Jiancheng Lv · Joey Tianyi Zhou

Conditions and Assumptions for Constraint-based Causal Structure Learning

Kayvan Sadeghi · Terry Soo

Flexible Model Aggregation for Quantile Regression

Rasool Fakoor · Taesup Kim · Jonas Mueller · Alexander Smola · Ryan Tibshirani

A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates

Yann Fraboni · Richard Vidal · Laetitia Kameni · Marco Lorenzi

Deep linear networks can benignly overfit when shallow ones do

Niladri S. Chatterji · Phil Long

Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization

Jianhao Ma · Salar Fattahi

Knowledge Hypergraph Embedding Meets Relational Algebra

Bahare Fatemi · Perouz Taslakian · David Vazquez · David Poole

A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models

Minwoo Chae · Dongha Kim · Yongdai Kim · Lizhen Lin

Minimal Width for Universal Property of Deep RNN

Chang hoon Song · Geonho Hwang · Jun ho Lee · Myungjoo Kang

Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments

Haixu Ma · Donglin Zeng · Yufeng Liu

Towards Learning to Imitate from a Single Video Demonstration

Glen Berseth · Florian Golemo · Christopher Pal

Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities

Brian R. Bartoldson · Bhavya Kailkhura · Davis Blalock

The multimarginal optimal transport formulation of adversarial multiclass classification

Nicolas Garcia Trillos · Matt Jacobs · Jakwang Kim

Sampling random graph homomorphisms and applications to network data analysis

Hanbaek Lyu · Facundo Memoli · David Sivakoff

Cluster-Specific Predictions with Multi-Task Gaussian Processes

Arthur Leroy · Pierre Latouche · Benjamin Guedj · Servane Gey

CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations

Gengchen Mai · Ni Lao · Yutong He · Jiaming Song · Stefano Ermon

Lifelong Language Pretraining with Distribution-Specialized Experts

Wuyang Chen · Yanqi Zhou · Nan Du · Yanping Huang · James Laudon · Zhifeng Chen · Claire Cui

PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search

Haibin Wang · Ce Ge · Hesen Chen · XIuyu Sun

Random Shuffle Transformer for Image Restoration

Jie Xiao · Xueyang Fu · Man Zhou · Hongjian Liu · Zheng-Jun Zha

Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection

Haoyue Bai · Gregory Canal · Xuefeng Du · Jeongyeol Kwon · Robert Nowak · Sharon Li

Investigating the Role of Model-Based Learning in Exploration and Transfer

Jacob C Walker · Eszter Vértes · Yazhe Li · Gabriel Dulac-Arnold · Ankesh Anand · Theophane Weber · Jessica Hamrick

Tractable Control for Autoregressive Language Generation

Honghua Zhang · Meihua Dang · Nanyun Peng · Guy Van den Broeck

Scaling of Class-wise Training Losses for Post-hoc Calibration

Seungjin Jung · Seungmo Seo · Yonghyun Jeong · Jongwon Choi

The Benefits of Mixup for Feature Learning

Difan Zou · Yuan Cao · Yuanzhi Li · Quanquan Gu

SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning

Junran Wu · Xueyuan Chen · Bowen Shi · Shangzhe Li · Ke Xu

Probabilistic Attention-to-Influence Neural Models for Event Sequences

Xiao Shou · DEBARUN BHATTACHARJYA · Tian Gao · Dharmashankar Subramanian · Oktie Hassanzadeh · Kristin Bennett

Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models

Harshit Varma · Abhijeet Awasthi · Sunita Sarawagi

Loss-Guided Diffusion Models for Plug-and-Play Controllable Generation

Jiaming Song · Qinsheng Zhang · Hongxu Yin · Morteza Mardani · Ming-Yu Liu · Jan Kautz · Yongxin Chen · Arash Vahdat

Disentangled Multi-Fidelity Deep Bayesian Active Learning

Dongxia Wu · Ruijia Niu · Matteo Chinazzi · Yian Ma · Rose Yu

Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations

Weiwei Lin · Chenhang He · Man-Wai Mak · Youzhi Tu

Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least

Siddharth Joshi · Baharan Mirzasoleiman

Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement Learning

Brett Daley · Martha White · Christopher Amato · Marlos C. Machado

Transformers as Algorithms: Generalization and Stability in In-context Learning

Yingcong Li · Muhammed Ildiz · Dimitris Papailiopoulos · Samet Oymak

Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features

Chieh Lin · Hung-Yu Tseng · Hsin-Ying Lee · Maneesh Singh · Ming-Hsuan Yang

Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach

Tri Nguyen · Shahana Ibrahim · Xiao Fu

Learning the Right Layers a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs

Sara Venturini · Andrea Cristofari · Francesco Rinaldi · Francesco Tudisco

Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation

Asuman Ozdaglar · Sarath Pattathil · Jiawei Zhang · Kaiqing Zhang

Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup

Muthu Chidambaram · Xiang Wang · Chenwei Wu · Rong Ge

SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series

Iris Huijben · Arthur A. Nijdam · Sebastiaan Overeem · Merel Van Gilst · Ruud J. G. van Sloun

Towards Trustworthy Explanation: On Causal Rationalization

Wenbo Zhang · TONG WU · Yunlong Wang · Yong Cai · Hengrui Cai

Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks

Xu Chu · Yujie Jin · Xin Wang · Shanghang Zhang · Yasha Wang · Wenwu Zhu · Hong Mei

Neural Algorithmic Reasoning with Causal Regularisation

Beatrice Bevilacqua · Kyriacos Nikiforou · Borja Ibarz · Ioana Bica · Michela Paganini · Charles Blundell · Jovana Mitrovic · Petar Veličković

Beam Tree Recursive Cells

Jishnu Ray Chowdhury · Cornelia Caragea

Bigger, Better, Faster: Human-level Atari with human-level efficiency

Max Schwarzer · Johan Obando Ceron · Aaron Courville · Marc Bellemare · Rishabh Agarwal · Pablo Samuel Castro

How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control

Jacopo Teneggi · Matthew Tivnan · Web Stayman · Jeremias Sulam

Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments

Runlong Zhou · Zhang Zihan · Simon Du

Constrained Efficient Global Optimization of Expensive Black-box Functions

Wenjie Xu · Yuning Jiang · Bratislav Svetozarevic · Colin Jones

Learning-Rate-Free Learning by D-Adaptation

Aaron Defazio · Konstantin Mishchenko

Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems

Ainesh Bakshi · Allen Liu · Ankur Moitra · morris yau

DUET: 2D Structured and Approximately Equivariant Representations

Xavi Suau · Federico Danieli · T. Anderson Keller · Arno Blaas · Chen Huang · Jason Ramapuram · Dan Busbridge · Luca Zappella

Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection

Marc Lafon · Elias Ramzi · Clément Rambour · Nicolas THOME

PaLM-E: An Embodied Multimodal Language Model

Danny Driess · Fei Xia · Mehdi S. M. Sajjadi · Corey Lynch · Aakanksha Chowdhery · Brian Ichter · Ayzaan Wahid · Jonathan Tompson · Quan Vuong · Tianhe (Kevin) Yu · Wenlong Huang · Yevgen Chebotar · Pierre Sermanet · Daniel Duckworth · Sergey Levine · Vincent Vanhoucke · Karol Hausman · Marc Toussaint · Klaus Greff · Andy Zeng · Igor Mordatch · Pete Florence

Language Instructed Reinforcement Learning for Human-AI Coordination

Hengyuan Hu · Dorsa Sadigh

ContraBAR: Contrastive Bayes-Adaptive Deep RL

Era Choshen · Aviv Tamar

Flash: Concept Drift Adaptation in Federated Learning

Kunjal Panchal · Sunav Choudhary · Subrata Mitra · Koyel Mukherjee · Somdeb Sarkhel · Saayan Mitra · Hui Guan

Revisiting Gradient Clipping: Stochastic bias and tight convergence guarantees

Anastasiia Koloskova · Hadrien Hendrikx · Sebastian Stich

Perturbation Analysis of Neural Collapse

Tom Tirer · Haoxiang Huang · Jonathan Niles-Weed

Towards Robust Graph Incremental Learning on Evolving Graphs

Junwei Su · Difan Zou · Zijun Zhang · Chuan Wu

Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation

Shengcao Cao · Mengtian Li · James Hays · Deva Ramanan · Yu-Xiong Wang · Liangyan Gui

Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication

Ajay Jaiswal · Shiwei Liu · Tianlong Chen · Ding · Zhangyang “Atlas” Wang

Interpretable Neural-Symbolic Concept Reasoning

Pietro Barbiero · Gabriele Ciravegna · Francesco Giannini · Mateo Espinosa Zarlenga · Lucie Charlotte Magister · Alberto Tonda · Pietro Lió · Frederic Precioso · Mateja Jamnik · Giuseppe Marra

Adversarial Collaborative Learning on Non-IID Features

Qinbin Li · Bingsheng He · Dawn Song

Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming

Jinuk Kim · Yeonwoo Jeong · Deokjae Lee · Hyun Oh Song

Gradient-based Wang--Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space

Weitang Liu · Yi-Zhuang You · Ying Wai Li · Jingbo Shang

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

Yilun Du · Conor Durkan · Robin Strudel · Josh Tenenbaum · Sander Dieleman · Rob Fergus · Jascha Sohl-Dickstein · Arnaud Doucet · Will Grathwohl

Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons

Rasmus Kjær Høier · D. Staudt · Christopher Zach

SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge

Mahdi Nikdan · Tommaso Pegolotti · Eugenia Iofinova · Eldar Kurtic · Dan Alistarh

Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time

Zichang Liu · Jue Wang · Tri Dao · Tianyi Zhou · Binhang Yuan · Zhao Song · Anshumali Shrivastava · Ce Zhang · Yuandong Tian · Christopher Re · Beidi Chen

Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning

Yuxin Tang · Zhimin Ding · Dimitrije Jankov · Binhang Yuan · Daniel Bourgeois · Chris Jermaine

A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

Mikael Henaff · Minqi Jiang · Roberta Raileanu

Ewald-based Long-Range Message Passing for Molecular Graphs

Arthur Kosmala · Johannes Gasteiger · Nicholas Gao · Stephan Günnemann

A Theoretical Analysis of the Learning Dynamics under Class Imbalance

Emanuele Francazi · Marco Baity-Jesi · Aurelien Lucchi

How much does Initialization Affect Generalization?

Sameera Ramasinghe · Lachlan E. MacDonald · Moshiur Farazi · Hemanth Saratchandran · Simon Lucey

Gradient Descent Monotonically Decreases the Sharpness of Gradient Flow Solutions in Scalar Networks and Beyond

Itai Kreisler · Mor Shpigel Nacson · Daniel Soudry · Yair Carmon

Towards Reliable Neural Specifications

Chuqin Geng · Van Nham Le · Xiaojie Xu · Zhaoyue Wang · Arie Gurfinkel · Xujie Si

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

ALEXANDRE DUVAL · Victor Schmidt · Alex Hernandez-Garcia · Santiago Miret · Fragkiskos Malliaros · Yoshua Bengio · David Rolnick

Feature Programming for Multivariate Time Series Prediction

Alex Reneau · Jerry Yao-Chieh Hu · Ammar Gilani · Han Liu

Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data

Cheol Jun Cho · Edward Chang · Gopala Anumanchipalli

Rethinking Visual Reconstruction: Experience-Based Content Completion Guided by Visual Cues

Jiaxuan Chen · Yu Qi · Gang Pan

LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation

Rui Xue · Haoyu Han · MohamadAli Torkamani · Jian Pei · Xiaorui Liu

Graph Positional Encoding via Random Feature Propagation

Moshe Eliasof · Fabrizio Frasca · Beatrice Bevilacqua · Eran Treister · Gal Chechik · Haggai Maron

Structure-informed Language Models Are Protein Designers

Zaixiang Zheng · Yifan Deng · Dongyu Xue · Yi Zhou · Fei YE · Quanquan Gu

Robust Camera Pose Refinement for Multi-Resolution Hash Encoding

Hwan Heo · Taekyung Kim · Jiyoung Lee · Jaewon Lee · Soohyun Kim · Hyunwoo Kim · Jin-Hwa Kim

Causal Structure Learning for Latent Intervened Non-stationary Data

Chenxi Liu · Kun Kuang

Robust Situational Reinforcement Learning in Face of Context Disturbances

Jinpeng Zhang · Yufeng Zheng · Chuheng Zhang · Li Zhao · Lei Song · Yuan Zhou · Jiang Bian

Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing

Jikai Jin · Zhiyuan Li · Kaifeng Lyu · Simon Du · Jason Lee

Non-stationary Reinforcement Learning under General Function Approximation

Songtao Feng · Ming Yin · Ruiquan Huang · Yu-Xiang Wang · Jing Yang · Yingbin LIANG

Iterative Approximate Cross-Validation

Yuetian Luo · Zhimei Ren · Rina Barber

A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment

Hanchen Xie · Jiageng Zhu · Mahyar Khayatkhoei · Jiazhi Li · Mohamed Hussein · Wael AbdAlmageed

Data Poisoning Attacks Against Multimodal Encoders

Ziqing Yang · Xinlei He · Zheng Li · Michael Backes · Mathias Humbert · Pascal Berrang · Yang Zhang

Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Chumeng Liang · Xiaoyu Wu · Yang Hua · Jiaru Zhang · Yiming Xue · Tao Song · Zhengui XUE · Ruhui Ma · Haibing Guan

Multi-View Masked World Models for Visual Robotic Manipulation

Younggyo Seo · Junsu Kim · Stephen James · Kimin Lee · Jinwoo Shin · Pieter Abbeel

Exploring the Benefits of Training Expert Language Models over Instruction Tuning

Joel Jang · Seungone Kim · Seonghyeon Ye · Doyoung Kim · Lajanugen Logeswaran · Moontae Lee · Kyungjae Lee · Minjoon Seo

AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

Haohe Liu · Zehua Chen · Yi Yuan · Xinhao Mei · Xubo Liu · Danilo Mandic · Wenwu Wang · Mark D Plumbley

Robust Weak Supervision with Variational Auto-Encoders

Francesco Tonolini · Nikolaos Aletras · Yunlong Jiao · Gabriella Kazai

Bidirectional Learning for Offline Model-based Biological Sequence Design

Can Chen · Yingxue Zhang · Xue Liu · Mark Coates

Fast Inference from Transformers via Speculative Decoding

Yaniv Leviathan · Matan Kalman · Yossi Matias

Action Matching: Learning Stochastic Dynamics from Samples

Kirill Neklyudov · Rob Brekelmans · Daniel Severo · Alireza Makhzani

Private Federated Learning with Autotuned Compression

Enayat Ullah · Christopher Choquette-Choo · Peter Kairouz · Sewoong Oh

Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption

Vasilii Feofanov · Malik TIOMOKO · Aladin Virmaux

A theory of representation learning gives a deep generalisation of kernel methods

Adam Yang · Maxime Robeyns · Edward Milsom · Ben Anson · Nandi Schoots · Laurence Aitchison

Optimistic Planning by Regularized Dynamic Programming

Antoine Moulin · Gergely Neu

Fast Combinatorial Algorithms for Min Max Correlation Clustering

Sami Davies · Benjamin Moseley · Heather Newman

Semi Bandit dynamics in Congestion Games: Convergence to Nash Equilibrium and No-Regret Guarantees.

Ioannis Panageas · EFSTRATIOS PANTELEIMON SKOULAKIS · Luca Viano · Xiao Wang · Volkan Cevher

Global Optimization with Parametric Function Approximation

Chong Liu · Yu-Xiang Wang

Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks

Mohammed Nowaz Rabbani Chowdhury · Shuai Zhang · Meng Wang · Sijia Liu · Pin-Yu Chen

Surrogate Module Learning: Reduce the Gradient Error Accumulation in Training Spiking Neural Networks

Shikuang Deng · Hao Lin · Yuhang Li · Shi Gu

One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale

Fan Bao · Shen Nie · Kaiwen Xue · Chongxuan Li · Shi Pu · Yaole Wang · Gang Yue · Yue Cao · Hang Su · Jun Zhu

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?

Yu Yao · Mingming Gong · Yuxuan Du · Jun Yu · Bo Han · Kun Zhang · Tongliang Liu

Active Learning based Structural Inference

Aoran Wang · Jun Pang

Superhuman Fairness

Omid Memarrast · Linh Vu · Brian Ziebart

PWSHAP: A Path-Wise Explanation Model for Targeted Variables

Lucile Ter-Minassian · Oscar Clivio · Karla DiazOrdaz · Robin Evans · Christopher Holmes

Covariate balancing using the integral probability metric for causal inference

Insung Kong · Yuha Park · Joonhyuk Jung · Kwonsang Lee · Yongdai Kim

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

Miruna Oprescu · Jacob Dorn · Marah Ghoummaid · Andrew Jesson · Nathan Kallus · Uri Shalit

Collaborative Causal Inference with Fair Incentives

Rui Qiao · Xinyi Xu · Bryan Kian Hsiang Low

WL meet VC

Christopher Morris · Floris Geerts · Jan M Tönshoff · Martin Grohe

Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization

Quanqi Hu · Zi-Hao Qiu · Zhishuai Guo · Lijun Zhang · Tianbao Yang

Refined Regret for Adversarial MDPs with Linear Function Approximation

Yan Dai · Haipeng Luo · Chen-Yu Wei · Julian Zimmert

User-level Private Stochastic Convex Optimization with Optimal Rates

Raef Bassily · Ziteng Sun

Delayed Bandits: When Do Intermediate Observations Help?

Emmanuel Esposito · Saeed Masoudian · Hao Qiu · Dirk van der Hoeven · Nicolò Cesa-Bianchi · Yevgeny Seldin

Bandit Online Linear Optimization with Hints and Queries

Aditya Bhaskara · Ashok Cutkosky · Ravi Kumar · Manish Purohit

Dimensionality Reduction for General KDE Mode Finding

Xinyu Luo · Christopher Musco · Cas Widdershoven

The Power of Uniform Sampling for k-Median

Lingxiao Huang · Shaofeng Jiang · Jianing Lou

Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps

Marco Cuturi · Michal Klein · Pierre Ablin

Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization

Ameya Velingker · Maximilian Vötsch · David Woodruff · Samson Zhou

Sketching Meets Differential Privacy: Fast Algorithm for Dynamic Kronecker Projection Maintenance

Zhao Song · Xin Yang · Yuanyuan Yang · Lichen Zhang

Reward-Mixing MDPs with Few Latent Contexts are Learnable

Jeongyeol Kwon · Yonathan Efroni · Constantine Caramanis · Shie Mannor

Near-Optimal Quantum Coreset Construction Algorithms for Clustering

Yecheng Xue · Xiaoyu Chen · Tongyang Li · Shaofeng Jiang

The Fast Johnson-Lindenstrauss Transform Is Even Faster

Ora Nova Fandina · Mikael Møller Høgsgaard · Kasper Green Larsen

Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation

Li'ang Li · Yifei duan · Guanghua Ji · Yongqiang Cai

Towards Theoretical Understanding of Inverse Reinforcement Learning

Alberto Maria Metelli · Filippo Lazzati · Marcello Restelli

Nearly Optimal Competitive Ratio for Online Allocation Problems with Two-sided Resource Constraints and Finite Requests

Qixin Zhang · Wenbing Ye · Zaiyi Chen · Haoyuan Hu · Enhong Chen · Yu Yang

Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit

Yeonju Ro · Zhangyang “Atlas” Wang · Vijay Chidambaram · Aditya Akella

Learning Temporally AbstractWorld Models without Online Experimentation

Benjamin Freed · Siddarth Venkatraman · Guillaume Sartoretti · Jeff Schneider · Howie Choset

Learning to Decouple Complex Systems

Zihan Zhou · Tianshu Yu

Regularization-free Diffeomorphic Temporal Alignment Nets

Ron Shapira Weber · Oren Freifeld

Deep Perturbation Learning: Enhancing the Network Performance via Image Perturbations

Zifan Song · Xiao Gong · Guosheng Hu · Cairong Zhao

Optimizing the Collaboration Structure in Cross-Silo Federated Learning

Wenxuan Bao · Haohan Wang · Jun Wu · Jingrui He

InfoOT: Information Maximizing Optimal Transport

Ching-Yao Chuang · Stefanie Jegelka · David Alvarez-Melis

Stabilizing Transformer Training by Preventing Attention Entropy Collapse

Shuangfei Zhai · Tatiana Likhomanenko · Etai Littwin · Dan Busbridge · Jason Ramapuram · Yizhe Zhang · Jiatao Gu · Joshua M Susskind

Diffusion Models for Black-Box Optimization

Siddarth Krishnamoorthy · Satvik Mehul Mashkaria · Aditya Grover

Learning Mixtures of Markov Chains and MDPs

Chinmaya Kausik · Kevin Tan · Ambuj Tewari

Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction

Khai Nguyen · Dang Nguyen · Nhat Ho

Importance Weighted Expectation-Maximization for Protein Sequence Design

Zhenqiao Song · Lei Li

Offline Meta Reinforcement Learning with In-Distribution Online Adaptation

Jianhao Wang · Jin Zhang · Haozhe Jiang · Junyu Zhang · Liwei Wang · Chongjie Zhang

X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion

Hanqing Zhao · Dianmo Sheng · Jianmin Bao · Dongdong Chen · Dong Chen · Fang Wen · Lu Yuan · Ce Liu · Wenbo Zhou · Qi Chu · Weiming Zhang · Nenghai Yu

Open-VCLIP: Transforming CLIP to an Open-vocabulary Video Model via Interpolated Weight Optimization

Zejia Weng · Xitong Yang · Ang Li · Zuxuan Wu · Yu-Gang Jiang

Data-Copying in Generative Models: A Formal Framework

Robi Bhattacharjee · Sanjoy Dasgupta · Kamalika Chaudhuri

Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement

Eden Saig · Nir Rosenfeld

The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data

Thomas Davies · Zhengchao Wan · Ruben Sanchez-Garcia

MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods

Ali Taghibakhshi · Nicolas Nytko · Tareq Uz Zaman · Scott MacLachlan · Luke Olson · Matthew West

Harmonic Neural Networks

Atiyo Ghosh · Antonio Gentile · Mario Dagrada · Chul Lee · Seong-hyok Sean Kim · Hyukgeun Cha · Yunjun Choi · Dongho Kim · JEONG-IL KYE · Vincent E Elfving

Representation-Driven Reinforcement Learning

Ofir Nabati · Guy Tennenholtz · Shie Mannor

Directed Chain Generative Adversarial Networks

Ming Min · Ruimeng Hu · Tomoyuki Ichiba

Weakly Supervised Regression with Interval Targets

Xin Cheng · Yuzhou Cao · Ximing Li · Bo An · Lei Feng

Weighted Sampling without Replacement for Deep Top-$k$ Classification

Dieqiao Feng · Yuanqi Du · Carla Gomes · Bart Selman

Are Diffusion Models Vulnerable to Membership Inference Attacks?

Jinhao Duan · Fei Kong · Shiqi Wang · Xiaoshuang Shi · Kaidi Xu

Blackout Diffusion: Generative Diffusion Models in Discrete-State Spaces

Javier E. Santos · Zachary Fox · Nicholas Lubbers · Yen Ting Lin

DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm

Yunhao Tang · Tadashi Kozuno · Mark Rowland · Anna Harutyunyan · Remi Munos · Bernardo Avila Pires · Michal Valko

Quantile Credit Assignment

Thomas Mesnard · Wenqi Chen · Alaa Saade · Yunhao Tang · Mark Rowland · Theophane Weber · Clare Lyle · Audrunas Gruslys · Michal Valko · Will Dabney · Georg Ostrovski · Eric Moulines · Remi Munos

Statistical Inference on Multi-armed Bandits with Delayed Feedback

Lei Shi · Jingshen Wang · Tianhao Wu

Multi-User Reinforcement Learning with Low Rank Rewards

Dheeraj Nagaraj · Suhas Kowshik · Naman Agarwal · Praneeth Netrapalli · Prateek Jain

Model-based Offline Reinforcement Learning with Count-based Conservatism

Byeongchan Kim · Min-hwan Oh

TIDE: Time Derivative Diffusion for Deep Learning on Graphs

Maysam Behmanesh · Maximilian Krahn · Maks Ovsjanikov

A Picture of the Space of Typical Learnable Tasks

Rahul Ramesh · Jialin Mao · Itay Griniasty · Rubing Yang · Han Kheng Teoh · Mark Transtrum · James Sethna · Pratik Chaudhari

Analyzing Diffusion as Serial Reproduction

Raja Marjieh · Ilia Sucholutsky · Thomas Langlois · Nori Jacoby · Thomas Griffiths

Formalizing Preferences Over Runtime Distributions

Devon Graham · Kevin Leyton-Brown · Tim Roughgarden

Principled Offline RL in the Presence of Rich Exogenous Information

Riashat Islam · Manan Tomar · Alex Lamb · Yonathan Efroni · Hongyu Zang · Aniket Didolkar · Dipendra Misra · Xin Li · Harm Seijen · Remi Tachet des Combes · John Langford

Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models

Phillip Rust · Anders Søgaard

Inverse Reinforcement Learning without Reinforcement Learning

Gokul Swamy · David Wu · Sanjiban Choudhury · J. Bagnell · Steven Wu

Team Belief DAG: Generalizing the Sequence Form to Team Games for Fast Computation of Correlated Team Max-Min Equilibria via Regret Minimization

Brian Zhang · Gabriele Farina · Tuomas Sandholm

Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model

Siyu Chen · Jibang Wu · Yifan Wu · Zhuoran Yang

How Bad is Top-$K$ Recommendation under Competing Content Creators?

Fan Yao · Chuanhao Li · Denis Nekipelov · Hongning Wang · Haifeng Xu

Task-Specific Skill Localization in Fine-tuned Language Models

Abhishek Panigrahi · Nikunj Saunshi · Haoyu Zhao · Sanjeev Arora

What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective

Rhea Chowers · Yair Weiss

A Three-regime Model of Network Pruning

Yefan Zhou · Yaoqing Yang · Arin Chang · Michael Mahoney

Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network

Farhad Pashakhanloo · Alexei Koulakov

SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning

Dongseok Shim · Seungjae Lee · H. Jin Kim

Uncertainty Estimation by Fisher Information-based Evidential Deep Learning

Danruo Deng · Guangyong Chen · Yang YU · Furui Liu · Pheng Ann Heng

Linkless Link Prediction via Relational Distillation

Zhichun Guo · William Shiao · Shichang Zhang · Yozen Liu · Nitesh Chawla · Neil Shah · Tong Zhao

Controlled Text Generation with Natural Language Instructions

Wangchunshu Zhou · Yuchen Jiang · Ethan Wilcox · Ryan Cotterell · Mrinmaya Sachan

Efficient Personalized Federated Learning via Sparse Model-Adaptation

Daoyuan Chen · Liuyi Yao · Dawei Gao · Bolin Ding · Yaliang Li

From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks

Cai Zhou · Xiyuan Wang · Muhan Zhang

On the Generalization of Multi-modal Contrastive Learning

Qi Zhang · Yifei Wang · Yisen Wang

On Pitfalls of Test-Time Adaptation

Hao Zhao · Yuejiang Liu · Alexandre Alahi · Tao Lin

Counterfactual Identifiability of Bijective Causal Models

Arash Nasr-Esfahany · Mohammad Alizadeh · Devavrat Shah

Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces

Zhengdao Chen

Taming graph kernels with random features

Krzysztof Choromanski

SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance

Amit Attia · Tomer Koren

An Instrumental Variable Approach to Confounded Off-Policy Evaluation

Yang Xu · Jin Zhu · Chengchun Shi · Shikai Luo · Rui Song

Fair yet Asymptotically Equal Collaborative Learning

Xiaoqiang Lin · Xinyi Xu · See-Kiong Ng · Chuan-Sheng Foo · Bryan Kian Hsiang Low

Fair and Accurate Decision Making through Group-Aware Learning

Ramtin Hosseini · Li Zhang · Bhanu Garg · Pengtao Xie

Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity

Xuxing Chen · Minhui Huang · Shiqian Ma · Krishna Balasubramanian

Matrix Estimation for Individual Fairness

Cindy Zhang · Sarah Cen · Devavrat Shah

Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA

Ilias Diakonikolas · Daniel Kane · Ankit Pensia · Thanasis Pittas

On the Impact of Knowledge Distillation for Model Interpretability

Hyeongrok Han · Siwon Kim · Hyun-Soo Choi · Sungroh Yoon

Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal

Emanuele Marconato · Gianpaolo Bontempo · ELISA FICARRA · Simone Calderara · Andrea Passerini · Stefano Teso

ClimaX: A foundation model for weather and climate

Tung Nguyen · Johannes Brandstetter · Ashish Kapoor · Jayesh K. Gupta · Aditya Grover

Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory

Justin Cui · Ruochen Wang · Si Si · Cho-Jui Hsieh

No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation

Feilong Zhang · Xianming Liu · Shiyi Lin · Gang Wu · Xiong Zhou · Junjun Jiang · Xiangyang Ji

Symmetry-Aware Robot Design with Structured Subgroups

Heng Dong · Junyu Zhang · Tonghan Wang · Chongjie Zhang

Traversing Between Modes in Function Space for Fast Ensembling

EungGu Yun · Hyungi Lee · Giung Nam · Juho Lee

Understanding and Defending Patched-based Adversarial Attacks for Vision Transformer

Liang Liu · Yanan Guo · Youtao Zhang · Jun Yang

Sequential Kernelized Independence Testing

Aleksandr Podkopaev · Patrick Bloebaum · Shiva Kasiviswanathan · Aaditya Ramdas

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning

Christopher Choquette-Choo · Hugh B McMahan · J K Rush · Abhradeep Guha Thakurta

Achieving Hierarchy-Free Approximation for Bilevel Programs with Equilibrium Constraints

Jiayang Li · Jing Yu · Boyi Liu · Yu Nie · Zhaoran Wang

On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances

Aritra Guha · Nhat Ho · XuanLong Nguyen

Label differential privacy and private training data release

Robert Busa-Fekete · andres munoz · Umar Syed · Sergei Vassilvitskii

Moccasin: Efficient Tensor Rematerialization for Neural Networks

Burak Bartan · Haoming Li · Harris Teague · Christopher Lott · Bistra Dilkina

Rigid Body Flows for Sampling Molecular Crystal Structures

Jonas Köhler · Michele Invernizzi · Pim de Haan · Frank Noe

The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning

Sarah Rathnam · Sonali Parbhoo · Weiwei Pan · Susan Murphy · Finale Doshi-Velez

Training Normalizing Flows from Dependent Data

Matthias Kirchler · Christoph Lippert · Marius Kloft

Bayesian online change point detection with Hilbert space approximate Student-t process

Jeremy Sellier · Petros Dellaportas

Input uncertainty propagation through trained neural networks

Paul Monchot · Loic Coquelin · Sébastien J. Petit · Sébastien Marmin · Erwann LE PENNEC · Nicolas Fischer

Principled Acceleration of Iterative Numerical Methods Using Machine Learning

Sohei Arisaka · Qianxiao Li

Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits

Sunrit Chakraborty · Saptarshi Roy · Ambuj Tewari

A Two-Stage Active Learning Algorithm for k-Nearest Neighbors

Nicholas Rittler · Kamalika Chaudhuri

Semi-Dual Unbalanced Quadratic Optimal Transport: fast statistical rates and convergent algorithm.

Adrien Vacher · François-Xavier Vialard

Contextual Conservative Interleaving Bandits

Kei Takemura

Are Neurons Actually Collapsed? On the Fine-Grained Structure in Neural Representations

Yongyi Yang · Jacob Steinhardt · Wei Hu

Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian

Haiyang Yu · Zhao Xu · Xiaofeng Qian · Xiaoning Qian · Shuiwang Ji

Graph Switching Dynamical Systems

Yongtuo Liu · Sara Magliacane · Miltiadis (Miltos) Kofinas · Efstratios Gavves

Probabilistic Concept Bottleneck Models

Eunji Kim · Dahuin Jung · Sangha Park · Siwon Kim · Sungroh Yoon

Function-Space Regularization in Neural Networks: A Probabilistic Perspective

Tim G. J. Rudner · Sanyam Kapoor · Shikai Qiu · Andrew Wilson

Parallel Online Clustering of Bandits via Hedonic Game

Xiaotong Cheng · Cheng Pan · Setareh Maghsudi

Towards Constituting Mathematical Structures for Learning to Optimize

Jialin Liu · Xiaohan Chen · Zhangyang “Atlas” Wang · Wotao Yin · HanQin Cai

Tighter Information-Theoretic Generalization Bounds from Supersamples

Ziqiao Wang · Yongyi Mao

Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs

Junkai Zhang · Weitong Zhang · Quanquan Gu

CLUTR: Curriculum Learning via Unsupervised Task Representation Learning

Abdus Salam Azad · Izzeddin Gur · Jasper Emhoff · Nathaniel Alexis · Aleksandra Faust · Pieter Abbeel · Ion Stoica

ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs

Ted Moskovitz · Brendan O'Donoghue · Vivek Veeriah · Sebastian Flennerhag · Satinder Singh · Tom Zahavy

Causal Bounds in Quasi-Markovian Graphs

Madhumitha Shridharan · Garud Iyengar

Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost

Marina Knittel · Max Springer · John P Dickerson · MohammadTaghi Hajiaghayi

SpotEM: Efficient Video Search for Episodic Memory

Santhosh Kumar Ramakrishnan · Ziad Al-Halah · Kristen Grauman

Provably Invariant Learning without Domain Information

Xiaoyu Tan · Yong LIN · Shengyu Zhu · Chao Qu · Xihe Qiu · Xu Yinghui · Peng Cui · Yuan Qi

Evaluating Unsupervised Denoising Requires Unsupervised Metrics

Adrià Marcos Morales · Matan Leibovich · Sreyas Mohan · Joshua Vincent · Piyush Haluai · Mai Tan · Peter Crozier · Carlos Fernandez-Granda

Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning

Thomas Miconi

Identifying Interpretable Subspaces in Image Representations

Neha Mukund Kalibhat · Shweta Bhardwaj · C. Bayan Bruss · Hamed Firooz · Maziar Sanjabi · Soheil Feizi

Causal Proxy Models for Concept-based Model Explanations

Zhengxuan Wu · Karel D'Oosterlinck · Atticus Geiger · Amir Zur · Christopher Potts

Neural Stochastic Differential Games for Time-series Analysis

Sungwoo Park · Byoungwoo Park · Moontae Lee · Changhee Lee

Implicit Neural Spatial Representations for Time-dependent PDEs

Honglin Chen · Rundi Wu · Eitan Grinspun · Changxi Zheng · Peter Yichen Chen

FARE: Provably Fair Representation Learning with Practical Certificates

Nikola Jovanović · Mislav Balunovic · Dimitar I. Dimitrov · Martin Vechev

Improving Visual Prompt Tuning for Self-supervised Vision Transformers

Seungryong Yoo · Eunji Kim · Dahuin Jung · JUNGBEOM LEE · Sungroh Yoon

On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline

Nicklas Hansen · Zhecheng Yuan · Yanjie Ze · Tongzhou Mu · Aravind Rajeswaran · Hao Su · Huazhe Xu · Xiaolong Wang

A Closer Look at Few-shot Classification Again

Xu Luo · Hao Wu · Ji Zhang · Lianli Gao · Jing Xu · Jingkuan Song

Bilevel Optimization with Coupled Decision-Dependent Distributions

Songtao Lu

Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input

Shokichi Takakura · Taiji Suzuki

Federated Linear Contextual Bandits with User-level Differential Privacy

Ruiquan Huang · Huanyu Zhang · Luca Melis · Milan Shen · Meisam Hejazinia · Jing Yang

Unconstrained Online Learning with Unbounded Losses

Andrew Jacobsen · Ashok Cutkosky

Doubly Optimal No-Regret Learning in Monotone Games

Yang Cai · Weiqiang Zheng

Brainformers: Trading Simplicity for Efficiency

Yanqi Zhou · Nan Du · Yanping Huang · Daiyi Peng · Chang Lan · Da Huang · Siamak Shakeri · David So · Andrew Dai · Yifeng Lu · Zhifeng Chen · Quoc Le · Claire Cui · James Laudon · Jeff Dean

A Modern Look at the Relationship between Sharpness and Generalization

Maksym Andriushchenko · Francesco Croce · Maximilian Müller · Matthias Hein · Nicolas Flammarion

SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning.

Tanguy MARCHAND · Regis Loeb · Ulysse Marteau-Ferey · Jean Ogier du Terrail · Arthur Pignet

Exploring the Limits of Model-Targeted Indiscriminate Data Poisoning Attacks

Yiwei Lu · Gautam Kamath · Yaoliang Yu

On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation

Zhanke Zhou · Chenyu Zhou · Xuan Li · Jiangchao Yao · QUANMING YAO · Bo Han

Progressive Purification for Instance-Dependent Partial Label Learning

Ning Xu · biao liu · JIAQI LYU · Congyu Qiao · Xin Geng

LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

WOOJUN KIM · Jeonghye Kim · Youngchul Sung

Multi-Objective GFlowNets

Moksh Jain · Sharath Chandra Raparthy · Alex Hernandez-Garcia · Jarrid Rector-Brooks · Yoshua Bengio · Santiago Miret · Emmanuel Bengio

Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling

Xinlu Zhang · Shiyang Li · Zhiyu Chen · Xifeng Yan · Linda Petzold

Long-Term Rhythmic Video Soundtracker

Jiashuo Yu · Yaohui Wang · Xinyuan Chen · Xiao Sun · Yu Qiao

Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift

Yijun Dong · Yuege Xie · Rachel Ward

Neural Diffusion Processes

Vincent Dutordoir · Alan Saul · Zoubin Ghahramani · Fergus Simpson

Estimating Joint Treatment Effects by Combining Multiple Experiments

Yonghan Jung · Jin Tian · Elias Bareinboim

Generalized Implicit Follow-The-Regularized-Leader

Keyi Chen · Francesco Orabona

PAL: Program-aided Language Models

Luyu Gao · Aman Madaan · Shuyan Zhou · Uri Alon · Pengfei Liu · Yiming Yang · Jamie Callan · Graham Neubig

Looped Transformers as Programmable Computers

Angeliki Giannou · Shashank Rajput · Jy-yong Sohn · Kangwook Lee · Jason Lee · Dimitris Papailiopoulos

Is Learning Summary Statistics Necessary for Likelihood-free Inference?

Yanzhi Chen · Michael Gutmann · Adrian Weller

The Computational Complexity of Concise Hypersphere Classification

Eduard Eiben · Robert Ganian · Iyad Kanj · Sebastian Ordyniak · Stefan Szeider

Partial Optimality in Cubic Correlation Clustering

David Stein · Silvia Di Gregorio · Bjoern Andres

Accelerated Stochastic Optimization Methods under Quasar-convexity

Qiang Fu · Dongchu Xu · Ashia Wilson

Off-Policy Average Reward Actor-Critic with Deterministic Policy Search

Naman Saxena · Subhojyoti Khastagir · Shishir Nadubettu Yadukumar · Shalabh Bhatnagar

Stochastic Gradient Descent under Markovian Sampling Schemes

Mathieu Even

Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions

Ezgi Korkmaz · Jonah Brown-Cohen

Estimation Beyond Data Reweighting: Kernel Method of Moments

Heiner Kremer · Yassine Nemmour · Bernhard Schölkopf · Jia-Jie Zhu

spred: Solving L1 Penalty with SGD

Liu Ziyin · Zihao Wang

Graph Neural Tangent Kernel: Convergence on Large Graphs

Sanjukta Krishnagopal · Luana Ruiz

Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification

Florian Heinrichs · Mavin Heim · Corinna Weber

Compositional Score Modeling for Simulation-Based Inference

Tomas Geffner · George Papamakarios · Andriy Mnih

Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data

Ali Siahkoohi · Rudy Morel · Maarten de Hoop · Erwan Allys · Gregory Sainton · Taichi Kawamura

Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems

Chawin Sitawarin · Florian Tramer · Nicholas Carlini

Performative Recommendation: Diversifying Content via Strategic Incentives

Itay Eilat · Nir Rosenfeld

"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts

Haoran Zhang · Harvineet Singh · Marzyeh Ghassemi · Shalmali Joshi

Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures

Oleg Balabanov · Matthias Beaupère · Laura Grigori · Victor Lederer

Latent Traversals in Generative Models as Potential Flows

Yue Song · T. Anderson Keller · Nicu Sebe · Max Welling

Competitive Gradient Optimization

Abhijeet Vyas · Brian Bullins · Kamyar Azizzadenesheli

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation

Mark Rowland · Yunhao Tang · Clare Lyle · Remi Munos · Marc Bellemare · Will Dabney

Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation

Yiming Cui · Linjie Yang · Haichao Yu

Human-Timescale Adaptation in an Open-Ended Task Space

Jakob Bauer · Kate Baumli · Feryal Behbahani · Avishkar Bhoopchand · Natalie Bradley-Schmieg · Michael Chang · Natalie Clay · Adrian Collister · Vibhavari Dasagi · Lucy Gonzalez · Karol Gregor · Edward Hughes · Sheleem Kashem · Maria Loks-Thompson · Hannah Openshaw · Jack Parker-Holder · Shreya Pathak · Nicolas Perez-Nieves · Nemanja Rakicevic · Tim Rocktäschel · Yannick Schroecker · Satinder Singh · Jakub Sygnowski · Karl Tuyls · Sarah York · Alexander Zacherl · Lei Zhang

Expected Gradients of Maxout Networks and Consequences to Parameter Initialization

Hanna Tseran · Guido Montufar

A Category-theoretical Meta-analysis of Definitions of Disentanglement

Yivan Zhang · Masashi Sugiyama

On Preemption and Learning in Stochastic Scheduling

Nadav Merlis · Hugo Richard · Flore Sentenac · Corentin Odic · Mathieu Molina · Vianney Perchet

Adversarial Cheap Talk

Christopher Lu · Timon Willi · Alistair Letcher · Jakob Foerster

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Yulun Jiang · Chen Liu · Zhichao Huang · Mathieu Salzmann · Sabine Süsstrunk

Improving the Model Consistency of Decentralized Federated Learning

Yifan Shi · Li Shen · Kang Wei · Yan Sun · Bo Yuan · Xueqian Wang · Dacheng Tao

One-Step Estimator for Permuted Sparse Recovery

Hang Zhang · Ping Li

Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints

Václav Voráček · Matthias Hein

Neural FIM for learning Fisher information metrics from point cloud data

Oluwadamilola Fasina · Guillaume Huguet · Alexander Tong · Yanlei Zhang · Guy Wolf · Maximilian Nickel · Ian Adelstein · Smita Krishnaswamy

Learn to Accumulate Evidence from All Training Samples: Theory and Practice

Deep Pandey · Qi Yu

IncDSI: Incrementally Updatable Document Retrieval

Varsha Kishore · Chao Wan · Justin Lovelace · Yoav Artzi · Kilian Weinberger

Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure

Ryoma Sato

The case for 4-bit precision: k-bit Inference Scaling Laws

Tim Dettmers · Luke Zettlemoyer

A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations

Bilal Chughtai · Lawrence Chan · Neel Nanda

Generated Graph Detection

Yihan Ma · Zhikun Zhang · Ning Yu · Xinlei He · Michael Backes · Yun Shen · Yang Zhang

LIV: Language-Image Representations and Rewards for Robotic Control

Yecheng Jason Ma · Vikash Kumar · Amy Zhang · Osbert Bastani · Dinesh Jayaraman

PromptBoosting: Black-Box Text Classification with Ten Forward Passes

Bairu Hou · Joe O'Connor · Jacob Andreas · Shiyu Chang · Yang Zhang

Learning Controllable Degradation for Real-World Super-Resolution via Constrained Flows

Seobin Park · Dongjin Kim · Sungyong Baik · Tae Hyun Kim

PASTA: Pessimistic Assortment Optimization

Juncheng Dong · Weibin Mo · Zhengling Qi · Cong Shi · Ethan Fang · Vahid Tarokh

Which Tricks are Important for Learning to Rank?

Ivan Lyzhin · Aleksei Ustimenko · Andrey Gulin · Liudmila Prokhorenkova

Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels

Fabian Altekrüger · Johannes Hertrich · Gabriele Steidl

Multicalibration as Boosting for Regression

Ira Globus-Harris · Declan Harrison · Michael Kearns · Aaron Roth · Jessica Sorrell

Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation

Siddharth Nagar Nayak · Kenneth Choi · Wenqi Ding · Sydney Dolan · Karthik Gopalakrishnan · Hamsa Balakrishnan

On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters

Wonyeol Lee · Sejun Park · Alex Aiken

Posterior Sampling for Deep Reinforcement Learning

Remo Sasso · Michelangelo Conserva · Paulo Rauber

Multi-Agent Best Arm Identification with Private Communications

Alexandre Rio · Merwan Barlier · Igor Colin · Marta Soare

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel

Mohamad Amin Mohamadi · Won Bae · Danica J Sutherland

Multi-agent Online Scheduling: MMS Allocations for Indivisible Items

Shengwei Zhou · Rufan Bai · Xiaowei Wu

Double-Weighting for Covariate Shift Adaptation

José I. Segovia-Martín · Santiago Mazuelas · Anqi Liu

Online Local Differential Private Quantile Inference via Self-normalization

Yi Liu · Qirui Hu · Lei Ding · Linglong Kong

Dink-Net: Neural Clustering on Large Graphs

Yue Liu · KE LIANG · Jun Xia · sihang zhou · xihong yang · Xinwang Liu · Stan Z Li

MABe22: A Multi-Species Multi-Task Benchmark for Learned Representations of Behavior

Jennifer J. Sun · Markus Marks · Andrew Ulmer · Dipam Chakraborty · Brian Geuther · Edward Hayes · Heng Jia · Vivek Kumar · Sebastian Oleszko · Zachary Partridge · Milan Peelman · Alice Robie · Catherine Schretter · Keith Sheppard · Chao Sun · Param Uttarwar · Julian Wagner · Erik Werner · Joseph Parker · Pietro Perona · Yisong Yue · Kristin Branson · Ann Kennedy

Fast Excess Risk Rates via Offset Rademacher Complexity

Chenguang Duan · Yuling Jiao · Lican Kang · Xiliang Lu · Jerry Yang

Settling the Reward Hypothesis

Michael Bowling · John Martin · David Abel · Will Dabney

Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving

Wei-Xin Li · Xiaodong Yang

Algorithms for bounding contribution for histogram estimation under user-level privacy

Yuhan Liu · Ananda Suresh · Wennan Zhu · Peter Kairouz · Marco Gruteser

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Invited Talk

Machine Learning with Social Purpose

Shakir Mohamed

4:00 PM - 5:00 PM

This talk talk has a single objective: to advocate for machine learning infused with social purpose. Social purpose here is an invitation to deepen our inquiries as investigators and inventors into the relationships between machine learning, our planet, and each other. In this way, social purpose transforms our field of machine learning: into something that is both technical and social. And my belief is that machine learning with social purpose will provide the passion and momentum for the contributions that are needed in overcoming the myriad of global challenges and in achieving our global goals. To make this all concrete, the talk will have three parts: machine learning for the Earth systems, sociotechnical AI, and strengthening global communities. And we’ll cover topics on generative models; evaluations and experts; healthcare and climate; fairness, ethics and safety; and bias and global inclusion. By the end, I hope we’ll have set the scene for a rich discussion on our responsibility and agency as researchers, and new ways of driving machine learning with social purpose.

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Speaker Bio

Shakir Mohamed works on technical and sociotechnical questions in machine learning research, working on problems in machine learning principles, applied problems in healthcare and environment, and ethics and diversity. Shakir is a Director for Research at DeepMind in London, an Associate Fellow at the Leverhulme Centre for the Future of Intelligence, and an Honorary Professor of University College London. Shakir is also a founder and trustee of the Deep Learning Indaba, a grassroots charity whose work is to build pan-African capacity and leadership in AI. Amongst other roles, Shakir served as the senior programme chair for ICLR 2021, and as the General Chair for NeurIPS 2022. Shakir also serves on the Board of Directors for some of the leading conferences in the field of machine learning and AI (ICML, ICLR, NeurIPS), is a member of the Royal Society diversity and inclusion committee, and on the international scientific advisory committee for the pan-Canadian AI strategy. Shakir is from South Africa, completed a postdoc at the University of British Columbia, received his PhD from the University of Cambridge, and received his masters and undergraduate degrees in Electrical and Information engineering from the University of the Witwatersrand, Johannesburg.

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Oral

Oral A1 Causal Learning, RL, Personalization

5:30 PM - 7:00 PM

10 Events in this session

Bayesian Design Principles for Frequentist Sequential Learning

Yunbei Xu · Assaf Zeevi

Towards Theoretical Understanding of Inverse Reinforcement Learning

Alberto Maria Metelli · Filippo Lazzati · Marcello Restelli

On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness

Haotian Ye · Xiaoyu Chen · Liwei Wang · Simon Du

Delayed Feedback in Kernel Bandits

Sattar Vakili · Danyal Ahmed · Alberto Bernacchia · Ciara Pike-Burke

Provably Learning Object-Centric Representations

Jack Brady · Roland S. Zimmermann · Yash Sharma · Bernhard Schölkopf · Julius von Kügelgen · Wieland Brendel

Task-specific experimental design for treatment effect estimation

Bethany Connolly · Kimberley Moore · Tobias Schwedes · Alexander Adam · Gary Willis · Ilya Feige · Christopher Frye

Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism.

Aude Sportisse · Hugo Schmutz · Olivier HUMBERT · Charles Bouveyron · Pierre-Alexandre Mattei

Interventional Causal Representation Learning

Kartik Ahuja · Divyat Mahajan · Yixin Wang · Yoshua Bengio

Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge

Shahine Bouabid · Jake Fawkes · Dino Sejdinovic

Sequential Underspecified Instrument Selection for Cause-Effect Estimation

Elisabeth Ailer · Jason Hartford · Niki Kilbertus

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Oral

Oral A6 Reinforcement Learning 2

5:30 PM - 7:00 PM

10 Events in this session

Learning GFlowNets From Partial Episodes For Improved Convergence And Stability

Kanika Madan · Jarrid Rector-Brooks · Maksym Korablyov · Emmanuel Bengio · Moksh Jain · Andrei-Cristian Nica · Tom Bosc · Yoshua Bengio · Nikolay Malkin

The Dormant Neuron Phenomenon in Deep Reinforcement Learning

Ghada Sokar · Rishabh Agarwal · Pablo Samuel Castro · Utku Evci

Reinforcement Learning from Passive Data via Latent Intentions

Dibya Ghosh · Chethan Bhateja · Sergey Levine

Best of Both Worlds Policy Optimization

Christoph Dann · Chen-Yu Wei · Julian Zimmert

Exponential Smoothing for Off-Policy Learning

Imad AOUALI · Victor-Emmanuel Brunel · David Rohde · Anna Korba

Quantile Credit Assignment

Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels

Sai Rajeswar · Pietro Mazzaglia · Tim Verbelen · Alex Piche · Bart Dhoedt · Aaron Courville · Alexandre Lacoste

Hierarchies of Reward Machines

Daniel Furelos-Blanco · Mark Law · Anders Jonsson · Krysia Broda · Alessandra Russo

Human-Timescale Adaptation in an Open-Ended Task Space

Settling the Reward Hypothesis

Michael Bowling · John Martin · David Abel · Will Dabney

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Oral

Oral A3 ML Theory

5:30 PM - 7:00 PM

11 Events in this session

Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains

Vishwaraj Doshi · Jie Hu · Do-Young Eun

Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond

Jaeyoung Cha · Jaewook Lee · Chulhee Yun

Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression

Yihao Xue · Siddharth Joshi · Eric Gan · Pin-Yu Chen · Baharan Mirzasoleiman

Tighter Information-Theoretic Generalization Bounds from Supersamples

Ziqiao Wang · Yongyi Mao

Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels

Simone Bombari · Shayan Kiyani · Marco Mondelli

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Hugo Cui · FLORENT KRZAKALA · Lenka Zdeborova

Why does Throwing Away Data Improve Worst-Group Error?

Kamalika Chaudhuri · Kartik Ahuja · Martin Arjovsky · David Lopez-Paz

Marginalization is not Marginal: No Bad VAE Local Minima when Learning Optimal Sparse Representations

David Wipf

Sharper Bounds for $\ell_p$ Sensitivity Sampling

David Woodruff · Taisuke Yasuda

AdaBoost is not an Optimal Weak to Strong Learner

Mikael Møller Høgsgaard · Kasper Green Larsen · Martin Ritzert

Generalization on the Unseen, Logic Reasoning and Degree Curriculum

Emmanuel Abbe · Samy Bengio · Aryo Lotfi · Kevin Rizk

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Oral

Oral A4 Diffusion

5:30 PM - 7:00 PM

10 Events in this session

AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners

Zhixuan Liang · Yao Mu · Mingyu Ding · Fei Ni · Masayoshi Tomizuka · Ping Luo

Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

Chumeng Liang · Xiaoyu Wu · Yang Hua · Jiaru Zhang · Yiming Xue · Tao Song · Zhengui XUE · Ruhui Ma · Haibing Guan

Graphically Structured Diffusion Models

Christian Weilbach · William Harvey · Frank Wood

Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?

Victor Boutin · Thomas FEL · Lakshya Singhal · Rishav Mukherji · Akash Nagaraj · Julien Colin · Thomas Serre

Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

Dongjun Kim · Yeongmin Kim · Se Jung Kwon · Wanmo Kang · IL CHUL MOON

Diffusion Models are Minimax Optimal Distribution Estimators

Kazusato Oko · Shunta Akiyama · Taiji Suzuki

GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration

Naoki Murata · Koichi Saito · Chieh-Hsin Lai · Yuhta Takida · Toshimitsu Uesaka · Yuki Mitsufuji · Stefano Ermon

OCD: Learning to Overfit with Conditional Diffusion Models

Shahar Lutati · Lior Wolf

Denoising MCMC for Accelerating Diffusion-Based Generative Models

Beomsu Kim · Jong Chul YE

Cones: Concept Neurons in Diffusion Models for Customized Generation

Zhiheng Liu · Ruili Feng · Kai Zhu · Yifei Zhang · Kecheng Zheng · Yu Liu · Deli Zhao · Jingren Zhou · Yang Cao

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Oral

Oral A2 Computer Vision and Efficient ML

5:30 PM - 7:00 PM

10 Events in this session

Raising the Cost of Malicious AI-Powered Image Editing

Hadi Salman · Alaa Khaddaj · Guillaume Leclerc · Andrew Ilyas · Aleksander Madry

Dynamics-inspired Neuromorphic Visual Representation Learning

Zhengqi Pei · Shuhui Wang

Scaling Vision Transformers to 22 Billion Parameters

Mostafa Dehghani · Josip Djolonga · Basil Mustafa · Piotr Padlewski · Jonathan Heek · Justin Gilmer · Andreas Steiner · Mathilde Caron · Robert Geirhos · Ibrahim Alabdulmohsin · Rodolphe Jenatton · Lucas Beyer · Michael Tschannen · Anurag Arnab · Xiao Wang · Carlos Riquelme · Matthias Minderer · Joan Puigcerver · Utku Evci · Manoj Kumar · Sjoerd van Steenkiste · Gamaleldin Elsayed · Aravindh Mahendran · Fisher Yu · Avital Oliver · Fantine Huot · Jasmijn Bastings · Mark Collier · Alexey Gritsenko · Vighnesh N Birodkar · Cristina Vasconcelos · Yi Tay · Thomas Mensink · Alexander Kolesnikov · Filip Pavetic · Dustin Tran · Thomas Kipf · Mario Lucic · Xiaohua Zhai · Daniel Keysers · Jeremiah Harmsen · Neil Houlsby

Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction

Andrey Savchenko

Fourmer: An Efficient Global Modeling Paradigm for Image Restoration

Man Zhou · Jie Huang · Chunle Guo · Chongyi Li

Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping

Baorui Ma · Yushen Liu · Zhizhong Han

Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles

Chaitanya Ryali · Yuan-Ting Hu · Daniel Bolya · Chen Wei · Haoqi Fan · Po-Yao Huang · Vaibhav Aggarwal · Arkabandhu Chowdhury · Omid Poursaeed · Judy Hoffman · Jitendra Malik · Yanghao Li · Christoph Feichtenhofer

Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch

Xunyi Zhao · Théotime Le Hellard · Lionel Eyraud-Dubois · Julia Gusak · Olivier Beaumont

SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge

Mahdi Nikdan · Tommaso Pegolotti · Eugenia Iofinova · Eldar Kurtic · Dan Alistarh

Fast Inference from Transformers via Speculative Decoding

Yaniv Leviathan · Matan Kalman · Yossi Matias

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Oral

Oral A5 Reinforcement Learning 1

5:30 PM - 7:00 PM

10 Events in this session

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark

Alexander Pan · Jun Shern Chan · Andy Zou · Nathaniel Li · Steven Basart · Thomas Woodside · Hanlin Zhang · Scott Emmons · Dan Hendrycks

Information-Theoretic State Space Model for Multi-View Reinforcement Learning

HyeongJoo Hwang · Seokin Seo · Youngsoo Jang · Sungyoon Kim · Geon-Hyeong Kim · Seunghoon Hong · Kee-Eung Kim

Reparameterized Policy Learning for Multimodal Trajectory Optimization

Zhiao Huang · Litian Liang · Zhan Ling · Xuanlin Li · Chuang Gan · Hao Su

Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL

Zakaria Mhammedi · Dylan Foster · Alexander Rakhlin

Subequivariant Graph Reinforcement Learning in 3D Environments

Runfa Chen · Jiaqi Han · Fuchun Sun · Wenbing Huang

A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

Mikael Henaff · Minqi Jiang · Roberta Raileanu

Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap

Hang Wang · Sen Lin · Junshan Zhang

Efficient RL via Disentangled Environment and Agent Representations

Kevin Gmelin · Shikhar Bahl · Russell Mendonca · Deepak Pathak

Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning

Sam Lobel · Akhil Bagaria · George Konidaris

On the Statistical Benefits of Temporal Difference Learning

David Cheikhi · Daniel Russo

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Main Navigation

Registration

Opening Remarks

Taking the Pulse Of Ethical ML in Health

Poster Session 1

Poster Session 2

Machine Learning with Social Purpose

Oral A1 Causal Learning, RL, Personalization

Oral A6 Reinforcement Learning 2

Oral A3 ML Theory

Oral A4 Diffusion

Oral A2 Computer Vision and Efficient ML

Oral A5 Reinforcement Learning 1