Conference Program
Monday, June 29
Workshops and Tutorials will take place on Monday. The complete schedule is TBD. For more information, please see the Workshops & Tutorials page.
Tuesday, June 30
Session 1 (8:30 – 9:55)
| Track 1 Bandits / Online Learning |
Track 2 High-Dimensional Statistics |
|---|---|
| 8:30 – 8:38 Truly Adapting to Adversarial Constraints in Constrained MABs Francesco Emanuele Stradi (Politecnico di Milano); Kalana Kalupahana (Politecnico di Milano); Matteo Castiglioni (Politecnico di Milano); Alberto Marchesi (Politecnico di Milano); Nicola Gatti (Politecnico di Milano) |
8:30 – 8:38 Finite Sample Bounds for Learning with Score Matching Devin Smedira (MIT); Abhijith Jayakumar (Los Alamos National Lab); Sidhant Misra (Los Alamos National Lab); Marc Vuffray (Los Alamos National Lab); Andrey Lokhov (Los Alamos National Lab) |
| 8:41 – 8:49 Adversarial Learning in Games with Bandit Feedback: Logarithmic Pure-Strategy Maximin Regret Shinji Ito (University of Tokyo); Haipeng Luo (University of Southern California); Arnab Maiti (University of Washington); Taira Tsuchiya (University of Tokyo); Yue Wu (University of Southern California) |
8:41 – 8:49 Rigorous Asymptotics for First-Order Algorithms Through the Dynamical Cavity Method Francisco Pernice (MIT); David Gamarnik (MIT); Yatin Dandi (EPFL); Lenka Zdeborova (EPFL) |
| 8:52 – 9:00 The Hidden Cost of Approximation in Online Mirror Descent Ofir Schlisselberg (Tel Aviv University); Uri Sherman (Tel Aviv University); Tomer Koren (Tel Aviv University and Google Research); Yishay Mansour ( Tel Aviv University and Google Research) |
8:52 – 9:00 Variational Tail Bounds for Norms of Random Vectors and Matrices Sohail Bahmani (Georgia Institute of Technology) |
| 9:03 – 9:11 Ambiguous Online Learning Vanessa Kosoy (Technion – Israel Institute of Technology) |
9:03 – 9:11 Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures Prashanti Anderson (MIT); Mitali Bafna (University of Washington); Rares-Darius Buhai (EPFL); Pravesh K. Kothari (Princeton University); David Steurer (ETH Zurich) |
| 9:14 – 9:22 A Simple, Optimal and Efficient Algorithm for Online Exp-Concave Optimization Yi-Han Wang (Nanjing University); Peng Zhao (Nanjing University); Zhi-Hua Zhou (Nanjing University) |
9:14 – 9:22 Overlap Analysis of the Shortest Path Problem: Local Search, Landscapes, and Franz–Parisi Potential Joonhyung Shin (University of Chicago); Frederic Koehler (University of Chicago) |
| 9:25 – 9:33 Learning from Equivalence Queries, Revisited Mark Braverman (Princeton); Roi Livni (Tel Aviv University); Yishay Mansour (Tel-Aviv University); Shay Moran (-); Kobbi Nissim (Georgetown) |
9:25 – 9:33 Almost sure null bankruptcy of test-by-betting strategies Hongjian Wang (Carnegie Mellon University); Shubhada Agrawal (Indian Institute of Science); Aaditya Ramdas (Carnegie Mellon University) |
| 9:36 – 9:44 Omniprediction with Long-Term Constraints Yahav Bechavod (University of Pennsylvania); Aaron Roth (University of Pennsylvania); Jiuyao Lu ( University of Pennsylvania) |
9:36 – 9:44 A Distribution Testing Approach to Clustering Distributions Gunjan Kumar (IIT Kanpur); Yash Pote (NUS); Jonathan Scarlett (National University of Singapore) |
| 9:47 – 9:55 Swap Regret Minimization Through Response-Based Approachability Ioannis Anagnostides (Carnegie Mellon University); Gabriele Farina (MIT); Maxwell Fishelson (MIT); Haipeng Luo (University of Southern California); Jon Schneider (Google) |
9:47 – 9:55 How fast can you find a good hypothesis? Anders Aamand (University of Copenhagen); Maryam Aliakbarpour (Rice University); Justin Chen (MIT); Sandeep Silwal (UW Madison) |
☕ Coffee Break (9:58 – 10:30)
Invited Talk (10:30 – 11:30): Shafi Goldwasser, MIT and UC Berkeley
Session 2 (11:35 – 12:27)
| Track 1 Bandits |
Track 2 High-Dimensional Statistics (Graphs) |
|---|---|
| 11:35 – 11:43 Adaptive Learning Rates with Surrogate Probability for Follow-the-Perturbed-Leader Jongyeong Lee (Korea Institute of Science and Technology); Junya Honda (Kyoto University, RIKEN AIP); Shinji Ito (The University of Tokyo, RIKEN AIP); Chansoo Kim (Korea Institute of Science and Technology) |
11:35 – 11:43 Information-Theoretic Thresholds for Bipartite Latent-Space Graphs Under Noisy Observations Andreas Göbel (Hasso Plattner Institute); Marcus Pappik (Hasso Plattner Institute); Leon Schiller (Hasso Plattner Institute) |
| 11:46 – 11:54 Learning Periodic Strategies in Blocking Bandits is as Hard as Bandits with Switching Costs Nicolò Cesa-Bianchi (Università degli Studi di Milano/Politecnico di Milano); Junya Honda (Kyoto University / RIKEN AIP); Yuko Kuroki (Intesa Sanpaolo AI Research); Atsushi Miyauchi (Intesa Sanpaolo); Lukas Zierahn (Centrum Wiskunde & Informatica/Booking.com) |
11:46 – 11:54 Spectral Recovery of a Planted Triangle-Dense Subgraph Sam van der Poel (Georgia Institute of Technology); Cheng Mao (Georgia Institute of Technology); Benjamin McKenna (Georgia Institute of Technology) |
| 11:57 – 12:05 Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach Hao Qiu (Università degli Studi di Milano); Mengxiao Zhang (University of Iowa); Nicolò Cesa-Bianchi (Università degli Studi di Milano) |
11:57 – 12:05 Recovery thresholds for hidden weighted sparse graphs Zhe Hou (Nanjing University); Jingcheng Liu (Nanjing University) |
| 12:08 – 12:16 On the Power of Adaptivity for $\varepsilon$-Best Arm Identification in Linear Bandits Arnab Maiti (University of Washington); Yunbei Xu (National University of Singapore); Kevin Jamieson (University of Washington) |
12:08 – 12:16 Robust Algorithms for Finding Cliques in Random Intersection Graphs via Sum-of-Squares Andreas Göbel (Hasso Plattner Institute); Janosch Ruff (Hasso Plattner Institute); Leon Schiller (Hasso Plattner Institute) |
| 12:19 – 12:27 Avoiding exp($k^*$) Scaling for Thompson Sampling in Combinatorial Semi-Bandits: From Multiple Seeds to a Single Seed Tianyuan Jin (National University of Singapore); Heyang Zhao (University of California, Los Angeles); Vincent Tan (National University of Singapore); Quanquan Gu (University of California, Los Angeles) |
12:19 – 12:27 Active Learning on Adversarially Corrupted Graphs Marco Bressan (Università degli Studi di Milano); Nicolò Cesa-Bianchi (Università degli Studi di Milano and Politecnico di Milano); Tommaso d'Orsi (Bocconi); Emmanuel Esposito (Università degli Studi di Milano); Silvio Lattanzi (Google Research) |
🍽 Lunch & Poster Session (12:30 – 14:00)
Session 3 (14:00 – 15:14)
| Track 1 Reinforcement Learning & Control |
Track 2 Optimization |
|---|---|
| 14:00 – 14:08 Learning to Reason with Curriculum I: Provable Benefits of Autocurriculum Nived Rajaraman (University of California, Berkeley); Audrey Huang (University of Illinois Urbana Champaign); Miro Dudik (Microsoft Research); Robert Schapire (Microsoft Research); Dylan J. Foster (Microsoft Research); Akshay Krishnamurthy (Microsoft Research) |
14:00 – 14:08 Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size Michal Derezinski (University of Michigan); Xiaoyu Dong (National University of Singapore) |
| 14:11 – 14:19 A Single Stepsize Suffices for Unprojected Linear TD(0): Simultaneous Robust and Fast Rates via Polyak–Ruppert Averaging Wei-Cheng Lee (Kaust); Francesco Orabona (Kaust) |
14:11 – 14:19 Fast, Parallel, Query-Efficient Binary Classification Ishani Karmarkar (Stanford University); Liam O'Carroll (Stanford University); Aaron Sidford (Stanford University) |
| 14:22 – 14:30 Continuous time policy evaluation is easier with noisy dynamics Samuel Robertson (University of Alberta); Thomas Newton (University of Waterloo); Csaba Szepesvari (University of Alberta) |
14:22 – 14:30 Learning Decision-Sufficient Representations for Linear Optimization Yuhan Ye (MIT); Saurabh Amin (MIT); Asuman Ozdaglar (MIT) |
| 14:33 – 14:41 Wasserstein Policy Learning for Distributional Outcomes Yiyan Huang (City University of Hong Kong); Cheuk Hang Leung (City University of Hong Kong); Qi Wu (City University of Hong Kong); Zhiheng Zhang (The Shanghai University of Finance and Economics) |
14:33 – 14:41 Random Reshuffling Dominates Stochastic Gradient Descent Zijian Liu (New York University) |
| 14:44 – 14:52 Strongly Polynomial Time Complexity of Policy Iteration for $L_\infty$ Robust MDPs Ali Asadi (Institute of Science and Technology Austria); Krishnendu Chatterjee (Institute of Science and Technology Austria); Ehsan Kafshdar Goharshady (Institute of Science and Technology Austria); Mehrdad Karrabi (Institute of Science and Technology Austria); Alipasha Montaseri (Institute of Science and Technology Austria); Carlo Pagano (Concordia University) |
14:44 – 14:52 Fast and Large-Scale Unbalanced Optimal Transport via its Semi-Dual and Adaptive Gradient Methods Ferdinand Genans (Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne Université) |
| 14:55 – 15:03 Randomization for Faster Exact Optimization of Discounted Markov Decision Processes Andrei Graur (Stanford University); Aaron Sidford (Stanford University); Ta-Wei Tu (Stanford University) |
14:55 – 15:03 High Probability Convergence Guarantees of Stochastic Gradient Descent Ascent in Structured Nonconvex Min-Max Games Junsoo Ha (Seoul National University) |
| 15:06 – 15:14 Optimal Variance-Dependent Regret Bounds for Infinite-Horizon MDPs Guy Zamir (University of Wisconsin-Madison); Matthew Zurek (University of Wisconsin-Madison); Yudong Chen (University of Wisconsin-Madison) |
15:06 – 15:14 How Does the ReLU Activation Affect the Implicit Bias of Gradient Descent on High-Dimensional Neural Network Regression? Kuo-Wei Lai (Georgia Institute of Technology); Guanghui Wang (Georgia Institute of Technology); Molei Tao (Georgia Institute of Technology); Vidya Muthukumar (Georgia Institute of Technology) |
☕ Coffee Break (15:17 – 15:40)
Session 4 (15:40 – 16:54)
| Track 1 Online Learning |
Track 2 Algebra & Structured Computation / Computational Complexity & Combinatorial Optimization |
|---|---|
| 15:40 – 15:48 Revisiting Gradient Equilibrium Nika Haghtalab (University of California, Berkeley); Michael Jordan (UC Berkeley); Brian Lee (UC Berkeley); Ryan Tibshirani (UC Berkeley) |
15:40 – 15:48 Efficient Learning and Symmetry Discovery under Exact Invariances Ashkan Soleymani (MIT); Behrooz Tahmasebi (MIT); Patrick Jaillet (MIT); Stefanie Jegelka (MIT) |
| 15:51 – 15:59 Language Generation with Infinite Contamination Anay Mehrotra (Stanford University); Grigoris Velegkas (Google Research); Xifan Yu (Yale University); Felix Zhou (Yale University) |
15:51 – 15:59 Data Augmentation: A Fourier Analysis Perspective Behrooz Tahmasebi (MIT); Melanie Weber (Harvard University); Stefanie Jegelka (MIT) |
| 16:02 – 16:10 Gradient-Variation Regret Bounds for Unconstrained Online Learning Yuheng Zhao (Nanjing University); Andrew Jacobsen (University of Milan); Nicolò Cesa-Bianchi (University of Milan); Peng Zhao (Nanjing University) |
16:02 – 16:10 Low-Degree Method Fails to Predict Robust Subspace Recovery He Jia (Northwestern University); Aravindan Vijayaraghavan (Northwestern University) |
| 16:13 – 16:21 Online Realizable Regression and Applications for ReLU Networks Ilan Doron-Arad (MIT); Idan Mehalel (The Hebrew University of Jerusalem); Elchanan Mossel (MIT) |
16:13 – 16:21 The monotonicity of the Franz-Parisi potential is equivalent with Low-degree MMSE lower bounds Konstantinos Tsirkas (Yale University); Leda Wang (Yale University); Ilias Zadik (Yale University) |
| 16:24 – 16:32 Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness Moise Blanchard (Georgia Institute of Technology); Alexander Rakhlin (MIT); Abhishek Shetty (MIT) |
16:24 – 16:32 Spectral Valleys and Sharp Failures in Greedy Determinant Maximization Rajiv Khanna (Purdue University) |
| 16:35 – 16:43 Optimal Reconstruction from Linear Queries Yuval Filmus (Technion); Shay Moran (Technion); Elizaveta Nesterova (Technion) |
16:35 – 16:43 Stable algorithms Lower Bounds for Estimation from MMSE Discontinuities Xifan Yu (Yale University); Ilias Zadik (Yale University) |
| 16:46 – 16:54 Computing Lewis weights to high precision using local relative smoothness Sander Gribling (Tilburg University); Aaron Sidford (Stanford University); Chenyi Zhang (Stanford) |
📋 Business Meeting (17:00 – 18:00)
Wednesday, July 1
Session 5 (8:30 – 10:06)
| Track 1 Online Learning |
Track 2 Statistical Learning Theory |
|---|---|
| 8:30 – 8:38 On the Importance of Randomization in Discriminative Feature Feedback Valentio Iverson (University Waterloo); Tosca Lechner (Vector Institute); Sivan Sabato (McMaster University) |
8:30 – 8:38 Risk Comparisons in Linear Regression: Implicit Regularization Dominates Explicit Regularization Jingfeng Wu (University of California, Berkeley); Peter Bartlett (University of California, Berkeley); Jason Lee (University of California, Berkeley); Sham Kakade (Harvard University); Bin Yu (University of California, Berkeley) |
| 8:41 – 8:49 Adaptive Matrix Online Learning through Smoothing with Guarantees for Nonsmooth Nonconvex Optimization Ruichen Jiang (UT Austin); Zakaria Mhammedi (Google Research); Mehryar Mohri (Google Research); Aryan Mokhtari (UT Austin) |
8:41 – 8:49 Recursively Enumerably Representable Classes and Computable Versions of the Fundamental Theorem of Statistical Learning David Kattermann (JKU Linz); Lothar Sebastian Krapp (University of Zurich) |
| 8:52 – 9:00 Distribution-Free Sequential Prediction with Abstentions Jialin Yu (Georgia Institute of Technology); Moise Blanchard (Georgia Institute of Technology) |
8:52 – 9:00 Second-Order Bounds for [0,1]-Valued Regression via Betting Loss Yinan Li (University of Arizona); Sungjoon Yoon (University of Arizona); Ethan Huang (University of Arizona); Kwang-Sung Jun (University of Arizona) |
| 9:03 – 9:11 Efficient Swap Multicalibration of Elicitable Properties Lunjia Hu (Northeastern University); Haipeng Luo (University of Southern California); Spandan Senapati (University of Southern California); Vatsal Sharan (University of Southern California) |
9:03 – 9:11 Learning Conditional Averages Marco Bressan (University of Milan); Nataly Brukhim (Institute for Advanced Study); Nicolò Cesa-Bianchi (University of Milan); Emmanuel Esposito (University of Milan); Yishay Mansour (Tel Aviv University and Google Research); Shay Moran (Technion and Google Research); Maximilian Thiessen (TU Wien) |
| 9:14 – 9:22 Space-Efficient Language Generation in the Limit Nicolas Flammarion (EPFL); Chirag Pabbaraju (Stanford); Hristo Papazov (EPFL); Miltiadis Stouras (EPFL); Ola Svensson (EPFL) |
9:14 – 9:22 Uniform Laws of Large Numbers in Product Spaces Ron Holzman (Technion); Shay Moran (Technion); Alexander Shlimovich (Technion) |
| 9:25 – 9:33 Online Learning with Simulators: No Regret in a Computationally Bounded World Sasha Voitovych (Massachusetts Institute of Technology); Alexander Rakhlin (Massachusetts Institute of Technology); Abhishek Shetty (Massachusetts Institute of Technology); Noah Golowich (Microsoft Research) |
9:25 – 9:33 A Perfectly Truthful Calibration Measure Jason Hartline (Northwestern University); Lunjia Hu (Northeastern University); Yifan Wu (Microsoft Research) |
| 9:36 – 9:44 Regret Minimization with Adaptive Opponents in Repeated Games Mingyang Liu (Massachusetts Institute of Technology); Asuman Ozdaglar (Massachusetts Institute of Technology); Tiancheng Yu (OpenAI); Kaiqing Zhang (University of Maryland, College Park) |
9:36 – 9:44 Adaptive Weighted Averaging Aditya Bhaskara (University of Utah); Ashok Cutkosky (Boston University); Ravi Kumar (Google); Manish Purohit (Google) |
| 9:47 – 9:55 Self-normalized martingales under smoothness assumption and uniform regret bounds for sequential linear regression Fan Chen (MIT); Jian Qian (HKU); Alexander Rakhlin (MIT); Nikita Zhivotovskiy (UCB) |
9:47 – 9:55 Sample-Efficient Omniprediction for Proper Losses Isaac Gibbs (University of California, Berkeley); Ryan Tibshirani (University of California, Berkeley) |
| 9:58 – 10:06 On Randomized Algorithms in Online Strategic Classification Chase Hutton (University of Maryland); Adam Melrod (Harvard University); Han Shao (University of Maryland) |
9:58 – 10:06 Statistical Learning from Attribution Sets Lorne Applebaum (Google); Robert Busa-Fekete (Google); August Chen (Cornell); Claudio Gentile (Google Research); Tomer Koren (Google and Tel Aviv University); Aryan Mokhtari (University of Texas at Austin) |
☕ Coffee Break (10:09 – 10:35)
Invited Talk (10:35 – 11:35): Maria-Florina Balcan, CMU
Session 6 (11:40 – 12:21)
| Track 1 Bandits |
Track 2 Quantum Learning |
|---|---|
| 11:40 – 11:48 Self-Concordant Perturbations for Linear Bandits Lucas Lévy (Ecole Polytechnique); Jean-Lou Valeau (ENSAE Paris); Arya Akhavan (University of Oxford); Patrick Rebeschini (University of Oxford) |
11:40 – 11:48 Learning depth-3 circuits via quantum agnostic boosting Srinivasan Arunachalam (IBM); Arkopal Dutt (IBM); Alexandru Gheorghiu (IBM); Michael de Oliveira (LIP6, Sorbonne Universite) |
| 11:51 – 11:59 On The Complexity of Best-Arm Identification in Non-Stationary Linear Bandits Leo Maynard-Zhang (University of Washington); Zhihan Xiong (University of Washington); Kevin Jamieson (University of Washington); Maryam Fazel (University of Washington) |
11:51 – 11:59 Cloning is as Hard as Learning for Stabilizer States Nikhil Bansal (University of Warwick); Matthias C. Caro (University of Warwick); Gaurav Mahajan (Yale University) |
| 12:02 – 12:10 An Information-Theoretic Analysis for Active Learning Abdellah Aznag (Columbia University); Adam Elmachtoub (Columbia University); Rachel Cummings (Columbia University) |
12:02 – 12:10 Information-Computation Gaps in Quantum Learning via Low-Degree Likelihood Sitan Chen (Harvard University); Weiyuan Gong (Harvard University); Jonas Haferkamp (Saarland University); Yihui Quek (EPFL) |
| 12:13 – 12:21 Sharp analysis of linear ensemble sampling Arya Akhavan (University of Oxford); David Janz (University of Alberta); Csaba Szepesvari (University of Alberta) |
12:13 – 12:21 Instance-optimal high-precision shadow tomography with few-copy measurements: A metrological approach Senrui Chen (California Institute of Technology); Weiyuan Gong (Harvard University); Sisi Zhou (University of Waterloo) |
🍽 Lunch & Poster Session (12:24 – 13:50)
Session 7 (13:50 – 15:26)
| Track 1 Online Learning / Reinforcement Learning & Control |
Track 2 Privacy |
|---|---|
| 13:50 – 13:58 Calibeating Made Simple Yurong Chen (Inria, Ecole Normale Supérieure, PSL Research University); Zhiyi Huang (The University of Hong Kong); Michael Jordan (Inria Paris; University of California, Berkeley); Haipeng Luo (University of Southern California) |
13:50 – 13:58 Minimax optimal differentially private synthetic data for smooth queries Rundong Ding (University of Southern California); Yiyun He (University of California San Diego); Yizhe Zhu (University of Southern California) |
| 14:01 – 14:09 Tight Sample Complexity Bounds for Entropic Best Policy Identification Amer Essakine (ENS Paris Saclay); Claire Vernade (University of technology Nuremberg) |
14:01 – 14:09 Nearly Linear-Time User-Level DP-SCO with Optimal Rates Badih Ghazi (Google); Ravi Kumar (Google); Daogao Liu (Google); Pasin Manurangsi (Google) |
| 14:12 – 14:20 Deep Q-Learning on H\"older Spaces Qian Qi (Peking University) |
14:12 – 14:20 Fixed-Parameter Tractability of Private Synthetic Data Generation Badih Ghazi (Google); Cristobal Guzman (Pontificia Universidad Católica de Chile); Pritish Kamath (Google); Alexander Knop (Google); Ravi Kumar (Google); Pasin Manurangsi (Google) |
| 14:23 – 14:31 Stochastic Safe Action Model Learning Zihao Deng (Amazon); Brendan Juba (Washington University in St Louis) |
14:23 – 14:31 On the Gradient Complexity of Private Optimization with Private Oracles Michael Menart (The Ohio State University); Aleksandar Nikolov (University of Toronto) |
| 14:34 – 14:42 Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear q-pi Realizability and Concentrability Volodymyr Tkachuk (University of Alberta); Csaba Szepesvári (University of Alberta); Xiaoqi Tan (University of Alberta) |
14:34 – 14:42 Differentially Private Language Generation in the Limit Anay Mehrotra (Stanford University); Grigoris Velegkas (Google Research); Xifan Yu (Yale University); Felix Zhou (Yale University) |
| 14:45 – 14:53 Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning HARIN LEE (University of Washington); Min-hwan Oh (Seoul National University) |
14:45 – 14:53 Ripple Mechanisms for Discrete and Private Statistics Matthew Joseph (Google); Alex Kulesza (Google Research); Yuyan Wang (Google Research); Alexander Yu (Google Research) |
| 14:56 – 15:04 Revisiting the (Sub)Optimality of Best-of-N for Inference-Time Alignment Ved Sriraman (Columbia University); Adam Block (Columbia University) |
14:56 – 15:04 On the Curse of Dimensionality in Private Sparse Covariance Estimation and PCA Syamantak Kumar (University of Texas at Austin); Shourya Pandey (University of Texas at Austin); Purnamrita Sarkar (University of Texas at Austin); Kevin Tian (University of Texas at Austin) |
| 15:07 – 15:15 Online Learning for Uninformed Markov Games: Empirical Nash-Value Regret and Non-Stationarity Adaptation Junyan Liu (University of Washington); Haipeng Luo (University of Southern California); Zihan Zhang (Hong Kong University of Science and Technology); Lillian Ratliff (University of Washington) |
15:07 – 15:15 Privately Estimating Black-Box Statistics Gunter Steinke (University of Canterbury); Thomas Steinke (Google) |
| 15:18 – 15:26 Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs Tianhao Wu (University of Wisconsin-Madison); Matthew Zurek (University of Wisconsin-Madison); Weina Wang (Carnegie Mellon University); Qiaomin Xie (University of Wisconsin-Madison) |
15:18 – 15:26 Private Linear Regression via a Down-Sensitivity to Privacy Reduction Ittai Rubinstein (Massachusetts Institute of Technology); Chris Ge ( Massachusetts Institute of Technology); Samuel Hopkins ( Massachusetts Institute of Technology) |
☕ Coffee Break (15:29 – 15:55)
Session 8 (15:55 – 16:58)
| Track 1 Online Learning |
Track 2 Statistical Learning Theory |
|---|---|
| 15:55 – 16:03 Toward Simultaneously Optimal Regret in U-Calibration Rafael Frongillo (University of Colorado Boulder); Haipeng Luo (USC); Nishant Mehta (Victoria); Jon Schneider (Google) |
15:55 – 16:03 Model Agreement via Anchoring Eric Eaton (University of Pennsylvania); Surbhi Goel (University of Pennsylvania); Marcel Hussing (University of Pennsylvania); Michael Kearns (University of Pennsylvania); Aaron Roth (University of Pennsylvania); Sikata Sengupta (University of Pennsylvania); Jessica Sorrell (Johns Hopkins University) |
| 16:06 – 16:14 Efficient High-Dimensional Online Outcome Indistinguishable Generative Models Gabriele Farina (MIT); Juan Perdomo (MIT) |
16:06 – 16:14 Boosting with List-Decodable Codes Addison Prairie (Stanford); Li-Yang Tan (Stanford) |
| 16:17 – 16:25 Worst-case Error Bounds for Online Learning of Smooth Functions Weian Xie (Massachusetts Institute of Technology) |
16:17 – 16:25 Tight list replicability bounds via a novel sphere covering theorem Ari Blondal (McGill University); Hamed Hatami (McGill University); Pooya Hatami (Ohio State University); Chavdar Lalov (Ohio State University); Sivan Tretiak (Ohio State University) |
| 16:28 – 16:36 A Characterization of List Language Identification in the Limit Moses Charikar (Stanford University); Chirag Pabbaraju (Stanford University); Ambuj Tewari (University of Michigan, Ann Arbor) |
16:28 – 16:36 Simultaneous Blackwell Approachability and Applications to Multiclass Omniprediction Lunjia Hu (Northeastern University); Kevin Tian (UT Austin); Chutong Yang (UT Austin) |
| 16:39 – 16:47 Near-optimal Swap Regret Minimization for Convex Losses Lunjia Hu (Harvard University); Jon Schneider (Google); Yifan Wu (Microsoft Research) |
16:39 – 16:47 Is Memorization Helpful or Harmful? Prior Information Sets the Threshold Chen Cheng (University of Chicago); Rina Barber (University of Chicago) |
| 16:50 – 16:58 Language Identification with Succinct Machine-Independent Traces Moses Charikar (Stanford University); Jon Kleinberg (Cornell University); Chirag Pabbaraju (Stanford University) |
16:50 – 16:58 Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget Michael Harding (University of Wisconsin-Madison); Kirthevasan Kandasamy (University of Wisconsin-Madison); Vikas Singh (University of Wisconsin-Madison) |
💡 Open Problems Session (17:05 – 17:57)
| 17:05 – 17:13 Does Differential Privacy Make PAC Learning Much Harder? Kobbi Nissim, Uri Stemmer, and Eliad Tsfadia |
| 17:16 – 17:24 Is Interaction Necessary for Order-Optimal 1-bit Mean Estimation? Ivan Lau and Jonathan Scarlett |
| 17:27 – 17:35 Online Optimization of Piecewise-Lipschitz Functions with Applications to Data-Driven Algorithm Design Maria-Florina Balcan, Wesley Pegden, and Dravyansh Sharma |
| 17:38 – 17:46 How much overparametrization is needed for ALS in tensor decomposition? Dionysis Arvanitakis, Vaidehi Srinivas, and Aravindan Vijayaraghavan |
| 17:49 – 17:57 Is the Power of Deep Learning over Linear Models Inherently Distribution Dependent? Vitaly Feldman, Pritish Kamath, and Nathan Srebro |
Thursday, July 2
Session 9 (8:30 – 10:06)
| Track 1 Statistical Learning Theory / Structured & Transfer Learning |
Track 2 Sampling |
|---|---|
| 8:30 – 8:38 Relatively Smart: A New Approach for Instance-Optimal Learning Alireza Pour (University of Waterloo); Shaddin Dughmi (University of Southern California) |
8:30 – 8:38 Polynomial-time sampling despite disorder chaos Eric Ma (Stanford); Tselil Schramm (Stanford) |
| 8:41 – 8:49 Minimax Limits of 𝑘 -Fold Cross-Validation via Majority Ido Nachum (University of Haifa); Rudiger Urbanke (EPFL); Thomas Weinberger (EPFL) |
8:41 – 8:49 The Geometry of Efficient Nonconvex Sampling Santosh Vempala (Georgia Institute of Technology); Andre Wibisono (Yale University) |
| 8:52 – 9:00 Margin in Abstract Spaces Yair Ashlagi (Tel Aviv University); Roi Livni (Tel Aviv University); Shay Moran (Technion, Google); Tom Waknine (Technion) |
8:52 – 9:00 Functional Stochastic Localization Anming Gu (UT Austin); Bobby Shi (UT Austin); Kevin Tian (UT Austin) |
| 9:03 – 9:11 On-Average Stability of Multipass SGD and Effective Dimension Simon Vary (Department of Statistics, University of Oxford); Tyler Farghly (Department of Statistics, University of Oxford); Ilja Kuzborskij (Google DeepMind); Patrick Rebeschini (Department of Statistics, University of Oxford) |
9:03 – 9:11 Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees Daniil Dmitriev (University of Pennsylvania); Zhihan Huang (University of Pennsylvania); Yuting Wei (University of Pennsylvania) |
| 9:14 – 9:22 Actively learning halfspaces without synthetic data Hadley Black (CUNY); Barna Saha (UCSD); Arya Mazumdar (UCSD); Kasper Larsen (Aarhus University); Geelon So (UCSD) |
9:14 – 9:22 Optimal Inference Schedules for Masked Diffusion Models Sitan Chen (Harvard University); Kevin Cong (Harvard University); Jerry Li (Washington University) |
| 9:25 – 9:33 Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities Alexandra Carpentier (Universität Potsdam); Christophe Giraud (Université Paris Saclay); Nicolas Verzelen (INRAE) |
9:25 – 9:33 High-accuracy log-concave sampling with stochastic gradients Fan Chen (MIT); Sinho Chewi (Yale University); Constantinos Daskalakis (MIT); Alexander Rakhlin (MIT) |
| 9:36 – 9:44 Optimal Hardness of Online Algorithms for Large Common Induced Subgraphs David Gamarnik (MIT); Miklos Racz (Northwestern University); Gabe Schoenbach (University of Chicago) |
9:36 – 9:44 Steering diffusion models with quadratic rewards: a fine-grained analysis Ankur Moitra (Massachusetts Institute of Technology); Andrej Risteski (CMU); Dhruv Rohatgi (Massachusetts Institute of Technology) |
| 9:47 – 9:55 An Exponential Lower Bound for Spectral Density Estimation on Unweighted Graphs Pan Peng (University of Science and Technology of China); Yuyang Wang (University of Science and Technology of China); Qiping Yang (The University of Sydney); Yichun Yang (Beijing Institute of Technology) |
9:47 – 9:55 Why is score-based sampling so effective? A general and adaptive reduction to SLC sub-problems Martin Wainwright (Massachusetts Institute of Technology) |
| 9:58 – 10:06 Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise Rafael Hanashiro (MIT); Abhishek Shetty (MIT); Patrick Jaillet (MIT) |
9:58 – 10:06 Almost Linear Convergence under Minimal Score Assumptions: Quantized Transition Diffusion Xunpeng Huang (The Hong Kong University of Science and Technology); Yingyu Lin (UCSD); Lijing Kuang (UCSD); Hanze Dong (Microsoft); Difan Zou (HKU); Yian Ma (UCSD); Tong Zhang (UIUC) |
☕ Coffee Break (10:09 – 10:35)
Invited Talk (10:35 – 11:35): Robert Schapire, Microsoft Research
Session 10 (11:40 – 12:21)
| Track 1 High-Dimensional Statistics (Graphs) |
Track 2 Robust Estimation |
|---|---|
| 11:40 – 11:48 Recovery of Planted Subgraphs Wasim Huleihel (Tel Aviv University) |
11:40 – 11:48 High-Dimensional Gaussian Mean Estimation under Realizable Contamination Ilias Diakonikolas (University of Wisconsin-Madison); Daniel Kane (University of California, San Diego); Thanasis Pittas (University of Wisconsin-Madison) |
| 11:51 – 11:59 Testing for a Hidden Geometry in Random Graphs Amit Silber (Tel Aviv University); Mor Oren ( Tel Aviv University); Wasim Huleihel (Tel Aviv University) |
11:51 – 11:59 Linear Regression under Missing or Corrupted Coordinates Ilias Diakonikolas (University of Wisconsin-Madison); Jelena Diakonikolas (University of Wisconsin-Madison); Daniel Kane (University of California, San Diego); Jasper Lee (University of California, Davis); Thanasis Pittas (University of Wisconsin-Madison) |
| 12:02 – 12:10 Rate-optimal community detection near the KS threshold via node-robust algorithms Jingqiu Ding (ETH Zurich); Yiding Hua (ETH Zurich); Kasper Lindberg (ETH Zurich); David Steurer (ETH Zurich); Aleksandr Storozhenko (Princeton University) |
12:02 – 12:10 A Quasi-Polynomial Time Mean Estimator Under Mean-Shift Contamination with Unknown Covariance Ilias Diakonikolas (University of Wisconsin Madison); Jingyi Gao (University of Wisconsin Madison); Giannis Iakovidis (University of Wisconsin Madison); Daniel Kane (University of California, San Diego); Sihan Liu (University of California, San Diego); Thanasis Pittas (University of Wisconsin Madison) |
| 12:13 – 12:21 Phase Transition in Convex Relaxations for Graph Alignment Laurent Massoulie (INRIA Paris); Sushil Mahavir Varma (INRIA Paris); Louis Vassaux (INRIA Paris); Irene Waldspurger (Université Paris-Dauphine) |
🍽 Lunch & Poster Session (12:24 – 14:00)
Session 11 (14:00 – 15:25)
| Track 1 Deep Learning Theory / Optimization |
Track 2 Distribution Learning and Statistical Learning Theory |
|---|---|
| 14:00 – 14:08 Graph neural networks extrapolate out-of-distribution for shortest paths Robert Nerem (University of California San Diego); Samantha Chen (University of California San Diego); Sanjoy Dasgupta (University of California San Diego); Yusu Wang (University of California San Diego) |
14:00 – 14:08 Density estimation for Hellinger via minimum-distance estimators: mixtures of Gaussians, log-concave, and more Spencer Compton (Stanford University); Jerry Li (University of Washington) |
| 14:11 – 14:19 Convergence of Continual Learning in Homogeneous Deep Networks Matan Schliserman (Blavatnik School of Computer Science, Tel Aviv University); Gon Buzaglo (Princeton University); Itay Evron (Meta); Daniel Soudry (Department of Electrical and Computing Engineering, Technion) |
14:11 – 14:19 Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions Maryam Aliakbarpour (Rice University); Alireza Azizi (Rice University); Ria Stevens (Rice University) |
| 14:22 – 14:30 Eigen-Spike Emergence, Quadratic Deterministic Equivalents, and the Classification of Nonlinearly-Separable Data Collin Cranston (University of California San Diego); Zhichao Wang (University of California San Diego); Todd Kemp (University of California San Diego); Michael Mahoney (University of California Berkeley) |
14:22 – 14:30 Learning Ising Models from Evolutions Jason Gaitonde (Duke University); Ankur Moitra (MIT); Elchanan Mossel (MIT) |
| 14:33 – 14:41 How Many Features Can a Language Model Store Under the Linear Representation Hypothesis? Nikhil Garg (Cornell Tech); Jon Kleinberg (Cornell University); Kenneth Peng (Cornell University) |
14:33 – 14:41 Estimating Ising Models in Total Variation Distance Constantinos Daskalakis (MIT); Vardis Kandiros (Columbia University); Rui Yao (MIT) |
| 14:44 – 14:52 Tight Sample Complexity of Transformers Chenxiao Yang (Toyota Technological Institute at Chicago); Nathan Srebro (Toyota Technological Institute at Chicago); Zhiyuan Li (Toyota Technological Institute at Chicago) |
14:44 – 14:52 DDPM Score Matching and Distribution Learning Sinho Chewi (Yale University); Alkis Kalavasis (Yale University); Anay Mehrotra (Yale University); Omar Montasser (Yale University) |
| 14:55 – 15:03 Optimal Learning-Rate Schedules under Functional Scaling Laws: Power Decay and Warmup-Stable-Decay Binghui Li (Peking University); Zilin Wang (Peking University); Fengling Chen (Peking University); Shiyang Zhao (Peking University); Ruiheng Zheng (Peking University); Lei Wu (Peking University) |
14:55 – 15:03 Diffusion-Network Alignment: An Efficient Algorithm and Explicit Probability Bounds Ziao Wang (University of Michigan, Ann Arbor); Lei Ying (University of Michigan, Ann Arbor) |
| 15:06 – 15:14 When Both Layers Learn: Training Dynamics of Representing Linear Models via ReLU Networks Berk Tinaz (University of Southern California); Changzhi Xie (University of Southern California); Mahdi Soltanolkotabi (University of Southern California) |
15:06 – 15:14 On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry Mohammad Tinati (University of Southern California); Stephen Tu (University of Southern California) |
| 15:17 – 15:25 On the implicit regularization of Langevin dynamics with projected noise Austin Stromme (Department of Statistics, CREST, ENSAE, IP Paris); Adrien Vacher (Department of Statistics, CREST, ENSAE, IP Paris); Govind Menon (Division of Applied Mathematics, Brown University and Institute for Advanced Study, Princeton) |
15:17 – 15:25 Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity Hengrui Luo (Rice University); Anna Ma (University of California Irvine); Ludovic Stephan (ENSAI-CREST); Yizhe Zhu (University of Southern California) |
☕ Coffee Break (15:28 – 15:55)
Session 12 (15:55 – 16:58)
| Track 1 Deep Learning Theory |
Track 2 Information Theory / Algebra & Structured Computation |
|---|---|
| 15:55 – 16:03 A Depth Hierarchy for Computing the Maximum in ReLU Networks via Extremal Graph Theory Itay Safran (Ben-Gurion University of the Negev) |
15:55 – 16:03 Optimal Sample Complexity Lower Bounds on Conditional Independence Testing Jan Seyfried (Centre for Quantum Technologies, National University of Singapore); Neelkanth Mishra (Department of Computer Science and Engineering, Indian Institute of Technology Delhi); Sayantan Sen (Centre for Quantum Technologies, National University of Singapore); Marco Tomamichel (Department of Electrical and Computer Engineering, Centre for Quantum Technologies, National University of Singapore) |
| 16:06 – 16:14 Optimal Neural Network Approximation of Smooth Compositional Functions on Sets with Low Intrinsic Dimension Thomas Nagler (LMU Munich); Sophie Langer (Ruhr-Universität Bochum) |
16:06 – 16:14 Partition Function Estimation under Bounded $f$-Divergence Adam Block (Columbia University); Abhishek Shetty (MIT) |
| 16:17 – 16:25 Theoretical Compression Bounds for Wide Multilayer Perceptrons houssam elcheairi (MIT); David Gamarnik (MIT); Rahul Mazumder (MIT) |
16:17 – 16:25 A Unified Lower Bound on the Noisy Query Complexity of Boolean Functions Yuzhou Gu (OpenAI); Xin Li (Johns Hopkins University); Yinzhan Xu (UCSD) |
| 16:28 – 16:36 Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining Yunwei Ren (Princeton University); Yatin Dandi (EPFL); Florent Krzakala (EPFL); Jason Lee (UC Berkeley) |
16:28 – 16:36 Reconstructing Riemannian Metrics From Random Geometric Graphs Han Huang (University of Missouri); Elchanan Mossel (MIT); Pakawut Jiradilok (Mahidol University International College) |
| 16:39 – 16:47 Limitations of SGD for Multi-Index Models Beyond Statistical Queries Daniel Barzilai (Weizmann Institute of Science); Ohad Shamir (Weizmann Institute of Science) |
16:39 – 16:47 Price of universality in vector quantization is at most 0.11 bit Alina Harbuzova (Massachusetts Institute of Technology); Or Ordentlich (Hebrew University of Jerusalem); Yury Polyanskiy (Massachusetts Institute of Technology) |
| 16:50 – 16:58 The Median is Easier than it Looks: Approximation with a Constant-Depth, Linear-Width ReLU Network Abhigyan Dutta (Purdue University); Itay Safran (Ben Gurion University of the Negev); Paul Valiant (Purdue University) |
16:50 – 16:58 The matrix-vector complexity of Ax=b Raphael Meyer (New York University); Ethan Epperly (UC Berkeley); Michał Dereziński (University of Michigan) |
🎤 Impromptu Session / Open Discussions (17:05 – 18:00)
Friday, July 3
Session 13 (8:30 – 10:06)
| Track 1 Optimization |
Track 2 Algebra & Structured Computation / Computational Complexity & Combinatorial Optimization |
|---|---|
| 8:30 – 8:38 Universality of high-dimensional scaling limits of stochastic gradient descent Aukosh Jagannath (University of Waterloo); Reza Gheissari (Northwestern University) |
8:30 – 8:38 On the Statistical Query Complexity of Learning Semiautomata: a Random Walk Approach George Giapitzakis (University of Waterloo); Kimon Fountoulakis (University of Waterloo); Eshaan Nichani (Princeton University); Jason Lee (University of California, Berkeley) |
| 8:41 – 8:49 Faster Newton Methods for Convex and Nonconvex Optimization in Gradient Complexity Lesi Chen (Tsinghua University); Chengchang Liu (The Chinese University of Hong Kong); Luo Luo (Fudan University); Jingzhao Zhang (Tsinghua University) |
8:41 – 8:49 Query Efficient Structured Matrix Learning Noah Amsel (NYU); Pratyush Avi (NYU); Tyler Chen (NYU); Feyza Duman Keles (NYU); Chinmay Hegde (NYU); Christopher Musco (NYU); Cameron Musco (University of Massachusetts, Amherst); David Persson (Flatiron Institute) |
| 8:52 – 9:00 Online Convex Optimization with Sublinear Noisy Probes Simone Di Gregorio (Sapienza University of Rome); Anupam Gupta (New York University); Stefano Leonardi (Sapienza University of Rome); Matteo Russo (EPFL) |
8:52 – 9:00 Sandwiching Polynomials for Geometric Concepts with Low Intrinsic Dimension Adam Klivans (UT Austin); Konstantinos Stavropoulos (UT Austin); Arsen Vasilyan (UT Austin) |
| 9:03 – 9:11 Tight Bounds for Logistic Regression with Large Stepsize Gradient Descent in Low Dimension Michael Crawshaw (George Mason University); Mingrui Liu (George Mason University) |
9:03 – 9:11 Compact Geometric Representations of Hierarchies Prashant Gokhale (University of Wisconsin-Madison); Piotr Indyk (MIT); Yuhao Liu (University of Wisconsin-Madison); Sandeep Silwal (University of Wisconsin-Madison); Tony Wang (University of Wisconsin-Madison); Haike Xu (MIT) |
| 9:14 – 9:22 Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization Aleksandar Armacki (École Polytechnique Fédérale de Lausanne); Dragana Bajović (Faculty of Technical Sciences, University of Novi Sad); Dušan Jakovetić (Faculty of Sciences, University of Novi Sad); Soummya Kar (Carnegie Mellon University); Ali H. Sayed (École Polytechnique Fédérale de Lausanne) |
9:14 – 9:22 Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift Adam Klivans (UT Austin); Shyamal Patel (Columbia University); Konstantinos Stavropoulos (UT Austin); Arsen Vasilyan (UT Austin) |
| 9:25 – 9:33 Clipping the Price of Adaptivity at the Tail Itai Kreisler (Tel Aviv University); Oliver Hinder (University of Pittsburgh); Yair Carmon (Tel Aviv University) |
9:25 – 9:33 Quiet Planting for k-SAT, Multiple Solutions of Arbitrary Geometry Ali Ahmadi (University of Maryland); Kiarash Banihashem (University of Maryland); Iman Gholami (University of Maryland); Mohammad Taghi Hajiaghayi (University of Maryland); Jan Olkowski (University of Maryland) |
| 9:36 – 9:44 Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time Qiang Fu (Yale University); Siddharth Mitra (Yale University); Vishwak Srinivasan (MIT); Xiuyuan Wang (Yale University); Andre Wibisono (Yale University); Ashia Wilson (MIT) |
9:36 – 9:44 Testing Noise Assumptions of Learning Algorithms Surbhi Goel (University of Pennsylvania); Adam Klivans (University of Texas at Austin); Konstantinos Stavropoulos (University of Texas at Austin); Arsen Vasilyan (University of Texas at Austin) |
| 9:47 – 9:55 Convergence Rates for Distribution Matching with Sliced Optimal Transport Gauthier Thurin (Université de Bordeaux); Claire Boyer (LMO - Orsay); Kimia Nadjahi (CNRS - ENS ) |
9:47 – 9:55 Algorithmic Thinking Theory MohammadHossein Bateni (Google); Vincent Cohen-Addad (Google); Yuzhou Gu (NYU); Silvio Lattanzi (Google); Simon Meierhans (ETH Zurich); Christopher Mohri (Stanford) |
| 9:58 – 10:06 On the Stability of Nonlinear Dynamics in GD and SGD: Beyond Quadratic Potentials Rotem Mulayoff (CISPA - Helmholtz Center for Information Security); Sebastian Stich (CISPA - Helmholtz Center for Information Security) |
📋 Poster Session (10:09 – 11:40)
Session 14 (11:40 – 12:43)
| Track 1 Bandits |
Track 2 High-Dimensional Statistics |
|---|---|
| 11:40 – 11:48 Optimism Stabilizes Thompson Sampling for Adaptive Inference Shunxing Yan (Peking University); Han Zhong (Peking University ) |
11:40 – 11:48 Separating Oblivious and Adaptive Models of Variable Selection Ziyun Chen (University of Washington); Jerry Li (University of Washington); Kevin Tian (University of Texas at Austin ); Yusong Zhu (University of Texas at Austin) |
| 11:51 – 11:59 The Sample Complexity of Multiclass and Sparse Contextual Bandits Liad Erez (Tel Aviv University); Fan Chen (MIT); Alexander Rakhlin (MIT); Tomer Koren (Tel Aviv University); Alon Cohen (Tel Aviv University); Yishay Mansour (Tel Aviv University); Shay Moran (Technion) |
11:51 – 11:59 On Efficient Robust Regression with Subquadratic Samples Deeksha Adil (ETH Zurich); Jaroslaw Blasiok (University of Bocconi); Hongjie Chen (ETH Zurich); Deepak Narayanan Sridharan (ETH Zurich) |
| 12:02 – 12:10 Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits Hao Qin (University of Arizona); Chicheng Zhang (University of Arizona) |
12:02 – 12:10 An Empirical Bayes Perspective on Heteroskedastic Mean Estimation Yanjun Han (New York University); Abhishek Shetty (MIT); Jacob Shkrob (New York University) |
| 12:13 – 12:21 Online Market Making and the Value of Observing the Order Book Davide Maran (Politecnico di Milano); Marcello Restelli (Politecnico di Milano) |
12:13 – 12:21 Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond alkis kalavasis (Yale University); Anay Mehrotra (Yale University); Felix Zhou (Yale University) |
| 12:24 – 12:32 Leveraging Similarities in Multi-Armed Bandits Khaled Eldowa (Inria); Thibaud Rahier (Criteo AI Lab); Cablant Augustin (Inria); Panayotis Mertikopoulos (CNRS); Pierre Gaillard (Inria) |
12:24 – 12:32 Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity Haitong Liu (ETH Zurich); Deepak Narayanan Sridharan (ETH Zurich); David Steurer (ETH Zurich); Manuel Wiedmer (ETH Zurich) |
| 12:35 – 12:43 A Tight Lower Bound for Non-stochastic Multi-armed Bandits with Expert Advice Zachary Chase (Kent State University); Shinji Ito (The University of Tokyo and RIKEN); Idan Mehalel (Technion) |
12:35 – 12:43 Universal priors: solving empirical Bayes via Bayesian inference and pretraining Nick Cannella (NYU); Anzo Teh (Massachusetts Institute of Technology); Yanjun Han (NYU); Yury Polyanskiy (MIT) |