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07/08/2023
Efficient Model-Free Exploration in Low-Rank MDPs
A major challenge in reinforcement learning is to develop practical, sam...
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05/01/2023
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
A central problem in the theory of multi-agent reinforcement learning (M...
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04/24/2023
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory
We consider the development of adaptive, instance-dependent algorithms f...
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04/12/2023
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL
We study the design of sample-efficient algorithms for reinforcement lea...
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03/22/2023
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
We consider the problem of decentralized multi-agent reinforcement learn...
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01/19/2023
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
A foundational problem in reinforcement learning and interactive decisio...
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11/25/2022
A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
We consider the problem of interactive decision making, encompassing str...
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10/09/2022
The Role of Coverage in Online Reinforcement Learning
Coverage conditions – which assert that the data logging distribution ad...
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07/12/2022
Contextual Bandits with Large Action Spaces: Made Practical
A central problem in sequential decision making is to develop algorithms...
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06/27/2022
On the Complexity of Adversarial Decision Making
A central problem in online learning and decision making – from bandits ...
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06/16/2022
Interaction-Grounded Learning with Action-inclusive Feedback
Consider the problem setting of Interaction-Grounded Learning (IGL), in ...
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06/09/2022
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information
In real-world reinforcement learning applications the learner's observat...
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12/27/2021
The Statistical Complexity of Interactive Decision Making
A fundamental challenge in interactive learning and decision making, ran...
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11/21/2021
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
We consider the offline reinforcement learning problem, where the aim is...
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09/21/2021
Minimax Rates for Conditional Density Estimation via Empirical Entropy
We consider the task of estimating a conditional density using i.i.d. sa...
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07/12/2021
Adapting to Misspecification in Contextual Bandits
A major research direction in contextual bandits is to develop algorithm...
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07/05/2021
Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
A recurring theme in statistical learning, online learning, and beyond i...
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04/14/2021
Eluder Dimension and Generalized Rank
We study the relationship between the eluder dimension for a function cl...
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01/11/2021
Independent Policy Gradient Methods for Competitive Reinforcement Learning
We obtain global, non-asymptotic convergence guarantees for independent ...
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10/08/2020
Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR...
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10/07/2020
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
In the classical multi-armed bandit problem, instance-dependent algorith...
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07/02/2020
Improved Bounds on Minimax Regret under Logarithmic Loss via Self-Concordance
We consider the classical problem of sequential probability assignment u...
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06/24/2020
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
We design an algorithm which finds an ϵ-approximate stationary point (wi...
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06/19/2020
Open Problem: Model Selection for Contextual Bandits
In statistical learning, algorithms for model selection allow the learne...
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04/30/2020
Learning nonlinear dynamical systems from a single trajectory
We introduce algorithms for learning nonlinear dynamical systems of the ...
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02/29/2020
Logarithmic Regret for Adversarial Online Control
We introduce a new algorithm for online linear-quadratic control in a kn...
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02/12/2020
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles
A fundamental challenge in contextual bandits is to develop flexible, ge...
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01/27/2020
Naive Exploration is Optimal for Online LQR
We consider the problem of online adaptive control of the linear quadrat...
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12/05/2019
Lower Bounds for Non-Convex Stochastic Optimization
We lower bound the complexity of finding ϵ-stationary points (with gradi...
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11/15/2019
ℓ_∞ Vector Contraction for Rademacher Complexity
We show that the Rademacher complexity of any R^K-valued function class ...
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06/03/2019
Model selection for contextual bandits
We introduce the problem of model selection for contextual bandits, wher...
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05/30/2019
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion
We study tensor completion in the agnostic setting. In the classical ten...
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04/09/2019
Hypothesis Set Stability and Generalization
We present an extensive study of generalization for data-dependent hypot...
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02/28/2019
Distributed Learning with Sublinear Communication
In distributed statistical learning, N samples are split across m machin...
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01/25/2019
Orthogonal Statistical Learning
We provide excess risk guarantees for statistical learning in the presen...
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10/25/2018
Uniform Convergence of Gradients for Non-Convex Learning and Optimization
We investigate 1) the rate at which refined properties of the empirical ...
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06/28/2018
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
We introduce a new family of margin-based regret guarantees for adversar...
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03/25/2018
Logistic Regression: The Importance of Being Improper
Learning linear predictors with the logistic loss---both in stochastic a...
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03/20/2018
Online Learning: Sufficient Statistics and the Burkholder Method
We uncover a fairly general principle in online learning: If regret can ...
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03/03/2018
Practical Contextual Bandits with Regression Oracles
A major challenge in contextual bandits is to design general-purpose alg...
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12/30/2017
Parameter-free online learning via model selection
We introduce an efficient algorithmic framework for model selection in o...
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06/26/2017
Spectrally-normalized margin bounds for neural networks
This paper presents a margin-based multiclass generalization bound for n...
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04/13/2017
ZigZag: A new approach to adaptive online learning
We develop a novel family of algorithms for the online learning setting ...
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08/21/2015