Accepted papers

  • Improved rates for prediction and identification for partially observed linear dynamical systems
    Holden Lee (Duke)
  • Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians
    Sami Davies (UW); Arya Mazumdar (University of California, San Diego); Cyrus Rashtchian (Google Research); Soumyabrata Pal (University of Massachusetts Amherst)
  • Efficient Local Planning with Linear Function Approximation
    Dong Yin (DeepMind); Botao Hao (Deepmind); Yasin Abbasi-Yadkori (DeepMind); Nevena Lazic (DeepMind); Csaba Szepesvari (DeepMind/University of Alberta)
  • The Mirror Langevin Algorithm Converges with Vanishing Bias
    Ruilin Li (Georgia Institute of Technology); Molei Tao (Georgia Institute of Technology); Santosh Vempala (Georgia Tech); Andre Wibisono (Yale University)
  • On the Initialization for Convex-Concave Min-max Problems
    Mingrui Liu (George Mason University); Francesco Orabona (Boston University)
  • Efficient Methods for Online Multiclass Logistic Regression
    Naman Agarwal (Google); Satyen Kale (Google); Julian Zimmert (Google)
  • Distinguishing Relational Pattern Languages With a Small Number of Short Strings
    Robert Holte (University of Alberta); Seyyed Mahmoud Mousavi (University of Regina); Sandra Zilles (University of Regina, Canada)
  • Social Learning in Non-Stationary Environments
    Etienne Boursier (ENS Paris Saclay); Vianney Perchet (ENSAE and Criteo AI Lab); Marco Scarsini (LUISS)
  • Infinitely Divisible Noise in the Low Privacy Regime
    Nina Mesing Stausholm (IT University of Copenhagen); Rasmus Pagh (University of Copenhagen)
  • Faster Rates of Differentially Private Stochastic Convex Optimization
    Jinyan Su (University of Electronic Science and Technology of China); Lijie Hu (King Abdullah University of Science and Technology); Di Wang (KAUST)
  • Efficient and Optimal Fixed-Time Regret with Two Experts
    Laura Greenstreet (University of British Columbia); Nick Harvey (University of British Columbia); Victor Portella (University of British Columbia)
  • Learning with distributional inverters
    Eric Binnendyk (New Mexico Institute of Mining and Technology); Marco L Carmosino (Boston University); Antonina Kolokolova (Memorial University Newfoundland); Ramyaa Ramyaa (New Mexico Institute of Mining and Technology); Manuel Sabin (UC Berkeley)
  • Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability
    Aadirupa Saha (Microsoft Research); Akshay Krishnamurthy (Microsoft)
  • Iterated Vector Fields and Conservatism, with Applications to Federated Learning
    Zachary Charles (Google Research); John K Rush (Google Research)
  • Global Riemannian Acceleration in Hyperbolic and Spherical Spaces
    David Martinez-Rubio (University of Oxford)
  • Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability
    Lunjia Hu (Stanford University); Charlotte Peale (Stanford University); Omer Reingold (Stanford University)
  • MISSO: Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems
    Belhal Karimi (Baidu Research); Hoi-To Wai (Chinese University of Hong Kong); Eric Moulines (Ecole Polytechnique); Ping Li (Baidu Research)
  • Adversarial Interpretation of Bayesian Inference
    Hisham Husain (Amazon); Jeremias Knoblauch (University College London)
  • TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions
    Gellert Weisz (DeepMind, UCL); Csaba Szepesvari (DeepMind/University of Alberta); Andras Gyorgy (DeepMind)
  • Scale-Free Adversarial Multi Armed Bandits
    Sudeep Raja Putta (Columbia University); Shipra Agrawal (Columbia University)
  • Algorithms for learning a mixture of linear classifiers
    Aidao Chen (Northwestern University); Anindya De (University of Pennsylvania); Aravindan Vijayaraghavan (Northwestern University)
  • Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally
    Peter Manohar (Carnegie Mellon University); Pravesh Kothari (Carnegie Mellon University); Brian H Zhang (Carnegie Mellon University)
  • Understanding Simultaneous Train and Test Robustness
    Pranjal Awasthi (Google); Sivaraman Balakrishnan (Carnegie Mellon University); Aravindan Vijayaraghavan (Northwestern University)
  • Multi-Agent Reinforcement Learning with Hierarchical Information Structure
    Hsu Kao (University of Michigan); Chen-Yu Wei (University of Southern California); Vijay Subramanian (University of Michigan)
  • Faster Noisy Power Method
    Zhiqiang Xu (Baidu); Ping Li (Baidu Research)
  • Faster Perturbed Stochastic Gradient Methods for Finding Local Minima
    Zixiang Chen (UCLA); Dongruo Zhou (UCLA); Quanquan Gu (University of California, Los Angeles)
  • Asymptotic Degradation of Linear Regression Estimates with Strategic Data Sources
    Benjamin Roussillon (Universite Grenoble-Alpes); Nicolas Gast (INRIA); Patrick Loiseau (Inria); Panayotis Mertikopoulos (CNRS and Criteo AI Lab)
  • Privacy Amplification via Shuffling for Linear Contextual Bandits
    Evrard Garcelon (Facebook); Kamalika Chaudhuri (University of California, San Diego); Vianney Perchet (ENSAE & Criteo AI Lab); Matteo Pirotta (Facebook AI Research)
  • A Model Selection Approach for Corruption Robust Reinforcement Learning
    Chen-Yu Wei (University of Southern California); Chris Dann (Google); Julian Zimmert (Google)
  • Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature
    Cynthia Dwork (Harvard); Michael P Kim (UC Berkeley); Omer Reingold (Stanford University); Guy N Rothblum (Weizmann Institute of Science); Gal Yona (Weizmann Institute of Science)
  • Universal Online Learning with Unbounded Losses: Memory Is All You Need
    Moise Blanchard (Massachusetts Institute of Technology); Romain  Cosson (MIT); Steve Hanneke (Purdue University)
  • Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets
    Depen Morwani (Indian Institute of Technology, Madras); Harish Guruprasad Ramaswamy (IIT Madras)
  • Distributed Online Learning for Joint Regret with Communication Constraints
    Dirk van der Hoeven (Universita degli Studi di Milano); Hedi Hadiji (University of Amsterdam); Tim van Erven (University of Amsterdam)
  • Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
    Benjamin Grimmer (Cornell University); Haihao Lu (University of Chicago); Pratik Worah (Google); Vahab Mirrokni (Google)
  • Universally Consistent Online Learning with Arbitrarily Dependent Responses
    Steve Hanneke (Purdue University)
  • Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation
    Zixiang Chen (UCLA); Dongruo Zhou (UCLA); Quanquan Gu (University of California, Los Angeles)
  • Implicit Parameter-free Online Learning with Truncated Linear Models
    Keyi Chen (Boston University); Ashok Cutkosky (Boston University); Francesco Orabona (Boston University)
  • Multicalibrated Partitions for Importance Weights
    Parikshit Gopalan (VMware Research); Omer Reingold (Stanford University); Vatsal Sharan (USC); Udi Wieder (VMware Research)
  • Learning what to remember
    Robi Bhattacharjee (University of California, San Diego); Gaurav Mahajan (University of California, San Diego)
  • On the Last Iterate Convergence of Momentum Methods
    Xiaoyu Li (Boston University); Mingrui Liu (George Mason University); Francesco Orabona (Boston University)
  • Leveraging Initial Hints for Free in Stochastic Linear Bandits
    Richard Zhang (Google Brain); Abhimanyu Das (Google); Ashok Cutkosky (Boston University); Chris Dann (Google)
  • Refined Lower Bounds for Nearest Neighbor Condensation
    Rajesh Chitnis (University of Birmingham)