ALT 2022

42 papers

A Model Selection Approach for Corruption Robust Reinforcement Learning Chen-Yu Wei, Christoph Dann, Julian Zimmert

Adversarial Interpretation of Bayesian Inference Hisham Husain, Jeremias Knoblauch

Algorithms for Learning a Mixture of Linear Classifiers Aidao Chen, Anindya De, Aravindan Vijayaraghavan

Almost Optimal Algorithms for Two-Player Zero-Sum Linear Mixture Markov Games Zixiang Chen, Dongruo Zhou, Quanquan Gu

Asymptotic Degradation of Linear Regression Estimates with Strategic Data Sources Benjamin Roussillon, Nicolas Gast, Patrick Loiseau, Panayotis Mertikopoulos

Beyond Bernoulli: Generating Random Outcomes That Cannot Be Distinguished from Nature Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, Gal Yona

Decentralized Cooperative Reinforcement Learning with Hierarchical Information Structure Hsu Kao, Chen-Yu Wei, Vijay Subramanian

Distinguishing Relational Pattern Languages with a Small Number of Short Strings Robert C. Holte, S. Mahmoud Mousawi, Sandra Zilles

Distributed Online Learning for Joint Regret with Communication Constraints Dirk Hoeven, Hédi Hadiji, Tim Erven

Efficient and Optimal Algorithms for Contextual Dueling Bandits Under Realizability Aadirupa Saha, Akshay Krishnamurthy

Efficient and Optimal Fixed-Time Regret with Two Experts Laura Greenstreet, Nicholas J. A. Harvey, Victor Sanches Portella

Efficient Local Planning with Linear Function Approximation Dong Yin, Botao Hao, Yasin Abbasi-Yadkori, Nevena Lazić, Csaba Szepesvári

Efficient Methods for Online Multiclass Logistic Regression Naman Agarwal, Satyen Kale, Julian Zimmert

Faster Noisy Power Method Zhiqiang Xu, Ping Li

Faster Perturbed Stochastic Gradient Methods for Finding Local Minima Zixiang Chen, Dongruo Zhou, Quanquan Gu

Faster Rates of Private Stochastic Convex Optimization Jinyan Su, Lijie Hu, Di Wang

Global Riemannian Acceleration in Hyperbolic and Spherical Spaces David Martínez-Rubio

Implicit Parameter-Free Online Learning with Truncated Linear Models Keyi Chen, Ashok Cutkosky, Francesco Orabona

Improved Rates for Prediction and Identification of Partially Observed Linear Dynamical Systems Holden Lee

Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets Depen Morwani, Harish G. Ramaswamy

Infinitely Divisible Noise in the Low Privacy Regime Rasmus Pagh, Nina Mesing Stausholm

Iterated Vector Fields and Conservatism, with Applications to Federated Learning Zachary Charles, Keith Rush

Learning What to Remember Robi Bhattacharjee, Gaurav Mahajan

Learning with Distributional Inverters Eric Binnendyk, Marco Carmosino, Antonina Kolokolova, R Ramyaa, Manuel Sabin

Leveraging Initial Hints for Free in Stochastic Linear Bandits Ashok Cutkosky, Chris Dann, Abhimanyu Das, Qiuyi Zhang

Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians Sami Davies, Arya Mazumdar, Soumyabrata Pal, Cyrus Rashtchian

Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability Lunjia Hu, Charlotte Peale, Omer Reingold

Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems Belhal Karimi, Hoi-To Wai, Eric Moulines, Ping Li

Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder

On the Initialization for Convex-Concave Min-Max Problems Mingrui Liu, Francesco Orabona

On the Last Iterate Convergence of Momentum Methods Xiaoyu Li, Mingrui Liu, Francesco Orabona

Polynomial-Time Sum-of-Squares Can Robustly Estimate Mean and Covariance of Gaussians Optimally Pravesh K. Kothari, Peter Manohar, Brian Hu Zhang

Privacy Amplification via Shuffling for Linear Contextual Bandits Evrard Garcelon, Kamalika Chaudhuri, Vianney Perchet, Matteo Pirotta

Refined Lower Bounds for Nearest Neighbor Condensation Rajesh Chitnis

Scale-Free Adversarial Multi Armed Bandits Sudeep Raja Putta, Shipra Agrawal

Social Learning in Non-Stationary Environments Etienne Boursier, Vianney Perchet, Marco Scarsini

TensorPlan and the Few Actions Lower Bound for Planning in MDPs Under Linear Realizability of Optimal Value Functions Gellért Weisz, Csaba Szepesvári, András György

The Mirror Langevin Algorithm Converges with Vanishing Bias Ruilin Li, Molei Tao, Santosh S. Vempala, Andre Wibisono

Understanding Simultaneous Train and Test Robustness Pranjal Awasthi, Sivaraman Balakrishnan, Aravindan Vijayaraghavan

Universal Online Learning with Unbounded Losses: Memory Is All You Need Moïse Blanchard, Romain Cosson, Steve Hanneke

Universally Consistent Online Learning with Arbitrarily Dependent Responses Steve Hanneke