research
∙
12/12/2022
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
We study reinforcement learning (RL) with linear function approximation....
share
research
∙
08/10/2022
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium
We consider learning Nash equilibria in two-player zero-sum Markov Games...
research
∙
05/23/2022
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs
Recent studies have shown that episodic reinforcement learning (RL) is n...
research
∙
02/28/2022
Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds
We consider learning a stochastic bandit model, where the reward functio...
research
∙
01/24/2022
Learning Contextual Bandits Through Perturbed Rewards
Thanks to the power of representation learning, neural contextual bandit...
research
∙
10/25/2021
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima
Escaping from saddle points and finding local minima is a central proble...
research
∙
10/25/2021
Linear Contextual Bandits with Adversarial Corruptions
We study the linear contextual bandit problem in the presence of adversa...
research
∙
10/12/2021
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
We study the model-based reward-free reinforcement learning with linear ...
research
∙
06/22/2021
Pure Exploration in Kernel and Neural Bandits
We study pure exploration in bandits, where the dimension of the feature...
research
∙
06/22/2021
Variance-Aware Off-Policy Evaluation with Linear Function Approximation
We study the off-policy evaluation (OPE) problem in reinforcement learni...
research
∙
06/22/2021
Provably Efficient Representation Learning in Low-rank Markov Decision Processes
The success of deep reinforcement learning (DRL) is due to the power of ...
research
∙
06/22/2021
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation
We study reinforcement learning (RL) with linear function approximation....
research
∙
02/25/2021
Batched Neural Bandits
In many sequential decision-making problems, the individuals are split i...
research
∙
02/17/2021
Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation
We study the reinforcement learning for finite-horizon episodic Markov d...
research
∙
02/15/2021
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation
We study reinforcement learning for two-player zero-sum Markov games wit...
research
∙
02/15/2021
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation
We study reinforcement learning in an infinite-horizon average-reward se...
research
∙
01/06/2021
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
We study reinforcement learning (RL) with linear function approximation ...
research
∙
12/15/2020
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
We study reinforcement learning (RL) with linear function approximation ...
research
∙
11/23/2020
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Reinforcement learning (RL) with linear function approximation has recei...
research
∙
11/19/2020
Provable Multi-Objective Reinforcement Learning with Generative Models
Multi-objective reinforcement learning (MORL) is an extension of ordinar...
research
∙
10/02/2020
Neural Thompson Sampling
Thompson Sampling (TS) is one of the most effective algorithms for solvi...
research
∙
10/01/2020
Minimax Optimal Reinforcement Learning for Discounted MDPs
We study the reinforcement learning problem for discounted Markov Decisi...
research
∙
06/23/2020
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
Modern tasks in reinforcement learning are always with large state and a...
research
∙
11/11/2019
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration
We study the stochastic contextual bandit problem, where the reward is g...
research
∙
01/31/2019
Stochastic Recursive Variance-Reduced Cubic Regularization Methods
Stochastic Variance-Reduced Cubic regularization (SVRC) algorithms have ...
research
∙
01/31/2019
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Smooth finite-sum optimization has been widely studied in both convex an...
research
∙
11/29/2018
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method
We propose a sample efficient stochastic variance-reduced cubic regulari...
research
∙
11/21/2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
We study the problem of training deep neural networks with Rectified Lin...
research
∙
08/16/2018
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
Adaptive gradient methods are workhorses in deep learning. However, the ...
research
∙
06/22/2018
Finding Local Minima via Stochastic Nested Variance Reduction
We propose two algorithms that can find local minima faster than the sta...
research
∙
06/20/2018
Stochastic Nested Variance Reduction for Nonconvex Optimization
We study finite-sum nonconvex optimization problems, where the objective...
research
∙
02/13/2018
