- 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)