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09/08/2023
Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity
Zero-sum Linear Quadratic (LQ) games are fundamental in optimal control ...
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06/26/2023
On Imitation in Mean-field Games
We explore the problem of imitation learning (IL) in the context of mean...
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06/25/2023
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
Trust-region methods based on Kullback-Leibler divergence are pervasivel...
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06/13/2023
Provably Learning Nash Policies in Constrained Markov Potential Games
Multi-agent reinforcement learning (MARL) addresses sequential decision-...
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06/12/2023
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes
Constrained Markov Decision Processes (CMDPs) are one of the common ways...
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06/02/2023
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space
We consider the reinforcement learning (RL) problem with general utiliti...
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05/18/2023
On the Statistical Efficiency of Mean Field Reinforcement Learning with General Function Approximation
In this paper, we study the statistical efficiency of Reinforcement Lear...
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02/26/2023
Kernel Conditional Moment Constraints for Confounding Robust Inference
We study policy evaluation of offline contextual bandits subject to unob...
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02/10/2023
Robust Knowledge Transfer in Tiered Reinforcement Learning
In this paper, we study the Tiered Reinforcement Learning setting, a par...
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02/03/2023
Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies
Recently, the impressive empirical success of policy gradient (PG) metho...
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12/29/2022
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
Mean-field games have been used as a theoretical tool to obtain an appro...
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11/14/2022
Learning to Optimize with Stochastic Dominance Constraints
In real-world decision-making, uncertainty is important yet difficult to...
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06/02/2022
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm
Natural actor-critic (NAC) and its variants, equipped with the represent...
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06/01/2022
Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization
We study differentially private (DP) algorithms for smooth stochastic mi...
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05/28/2022
Uniform Convergence and Generalization for Nonconvex Stochastic Minimax Problems
This paper studies the uniform convergence and generalization bounds for...
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05/25/2022
Stochastic Second-Order Methods Provably Beat SGD For Gradient-Dominated Functions
We study the performance of Stochastic Cubic Regularized Newton (SCRN) o...
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05/17/2022
Adaptive Momentum-Based Policy Gradient with Second-Order Information
The variance reduced gradient estimators for policy gradient methods has...
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02/20/2022
Learning to Control Partially Observed Systems with Finite Memory
We consider the reinforcement learning problem for partially observed Ma...
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01/19/2022
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
We study the bilinearly coupled minimax problem: min_xmax_y f(x) + y^⊤ A...
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12/10/2021
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity
Gradient descent ascent (GDA), the simplest single-loop algorithm for no...
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06/08/2021
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
Natural policy gradient (NPG) methods with function approximation achiev...
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03/29/2021
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization
This paper studies the complexity for finding approximate stationary poi...
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03/14/2021
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction
The building sector consumes the largest energy in the world, and there ...
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03/02/2021
Sample Complexity and Overparameterization Bounds for Projection-Free Neural TD Learning
We study the dynamics of temporal-difference learning with neural networ...
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07/09/2020
Provably-Efficient Double Q-Learning
In this paper, we establish a theoretical comparison between the asympto...
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02/25/2020
Biased Stochastic Gradient Descent for Conditional Stochastic Optimization
Conditional Stochastic Optimization (CSO) covers a variety of applicatio...
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02/23/2020
Periodic Q-Learning
The use of target networks is a common practice in deep reinforcement le...
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02/22/2020
Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problems
Nonconvex minimax problems appear frequently in emerging machine learnin...
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12/04/2019
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms
In this paper, we introduce a unified framework for analyzing a large fa...
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12/01/2019
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
This article reviews recent advances in multi-agent reinforcement learni...
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05/28/2019
Sample Complexity of Sample Average Approximation for Conditional Stochastic Optimization
In this paper, we study a class of stochastic optimization problems, ref...
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04/27/2019
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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04/24/2019
Target-Based Temporal Difference Learning
The use of target networks has been a popular and key component of recen...
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02/26/2019
Quadratic Decomposable Submodular Function Minimization: Theory and Practice
We introduce a new convex optimization problem, termed quadratic decompo...
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11/06/2018
Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum log-likelihood estimation for exponenti...
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06/26/2018
Quadratic Decomposable Submodular Function Minimization
We introduce a new convex optimization problem, termed quadratic decompo...
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01/25/2018
Nonparametric Hawkes Processes: Online Estimation and Generalization Bounds
In this paper, we design a nonparametric online algorithm for estimating...
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12/29/2017
Smoothed Dual Embedding Control
We revisit the Bellman optimality equation with Nesterov's smoothing tec...
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12/29/2017
Boosting the Actor with Dual Critic
This paper proposes a new actor-critic-style algorithm called Dual Actor...
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01/11/2017
Stochastic Generative Hashing
Learning-based binary hashing has become a powerful paradigm for fast se...
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08/03/2016
Fast and Simple Optimization for Poisson Likelihood Models
Poisson likelihood models have been prevalently used in imaging, social ...
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07/15/2016
Learning from Conditional Distributions via Dual Embeddings
Many machine learning tasks, such as learning with invariance and policy...
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06/09/2015
Provable Bayesian Inference via Particle Mirror Descent
Bayesian methods are appealing in their flexibility in modeling complex ...
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07/21/2014
Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neur...
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11/03/2012