Zhengyuan Zhou

research

∙ 05/28/2023

Sample Complexity of Variance-reduced Distributionally Robust Q-learning

Dynamic decision making under distributional shifts is of fundamental in...

0 Shengbo Wang, et al. ∙

research

∙ 03/22/2023

Stochastic Nonsmooth Convex Optimization with Heavy-Tailed Noises

Recently, several studies consider the stochastic optimization problem b...

0 Zijian Liu, et al. ∙

research

∙ 02/26/2023

A Finite Sample Complexity Bound for Distributionally Robust Q-learning

We consider a reinforcement learning setting in which the deployment env...

0 Shengbo Wang, et al. ∙

research

∙ 02/14/2023

Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise

We consider the stochastic optimization problem with smooth but not nece...

0 Zijian Liu, et al. ∙

research

∙ 02/13/2023

Near-Optimal High-Probability Convergence for Non-Convex Stochastic Optimization with Variance Reduction

Traditional analyses for non-convex stochastic optimization problems cha...

0 Zijian Liu, et al. ∙

research

∙ 01/27/2023

Single-Trajectory Distributionally Robust Reinforcement Learning

As a framework for sequential decision-making, Reinforcement Learning (R...

0 Zhipeng Liang, et al. ∙

research

∙ 11/05/2022

Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions

With the advent and increasing consolidation of e-commerce, digital adve...

0 Wei Zhang, et al. ∙

research

∙ 09/14/2022

Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation

Among the reasons hindering reinforcement learning (RL) applications to ...

0 Xiaoteng Ma, et al. ∙

research

∙ 09/02/2022

Optimal Diagonal Preconditioning: Theory and Practice

Preconditioning has been a staple technique in optimization and machine ...

5 Zhaonan Qu, et al. ∙

research

∙ 07/10/2022

Learning to Order for Inventory Systems with Lost Sales and Uncertain Supplies

We consider a stochastic lost-sales inventory control system with a lead...

0 Boxiao Chen, et al. ∙

research

∙ 03/08/2022

Sensitivity Analysis under the f-Sensitivity Models: Definition, Estimation and Inference

The digitization of the economy has witnessed an explosive growth of ava...

0 Ying Jin, et al. ∙

research

∙ 02/19/2022

Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning

Off-policy evaluation and learning (OPE/L) use offline observational dat...

0 Nathan Kallus, et al. ∙

research

∙ 12/06/2021

Optimal No-Regret Learning in Strongly Monotone Games with Bandit Feedback

We consider online no-regret learning in unknown games with bandit feedb...

0 Tianyi Lin, et al. ∙

research

∙ 07/08/2021

Computational Benefits of Intermediate Rewards for Hierarchical Planning

Many hierarchical reinforcement learning (RL) applications have empirica...

0 Yuexiang Zhai, et al. ∙

research

∙ 07/06/2021

Distributed stochastic optimization with large delays

One of the most widely used methods for solving large-scale stochastic o...

12 Zhengyuan Zhou, et al. ∙

research

∙ 05/05/2021

Policy Learning with Adaptively Collected Data

Learning optimal policies from historical data enables the gains from pe...

21 Ruohan Zhan, et al. ∙

research

∙ 03/08/2021

No Discounted-Regret Learning in Adversarial Bandits with Delays

Consider a player that in each round t out of T rounds chooses an action...

0 Ilai Bistritz, et al. ∙

research

∙ 03/07/2021

Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities

The optimization problems associated with training generative adversaria...

0 Chaobing Song, et al. ∙

research

∙ 02/25/2021

Doubly-Adaptive Thompson Sampling for Multi-Armed and Contextual Bandits

To balance exploration and exploitation, multi-armed bandit algorithms n...

0 Maria Dimakopoulou, et al. ∙

research

∙ 02/10/2021

Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State

We design a simple reinforcement learning agent that, with a specificati...

0 Shi Dong, et al. ∙

research

∙ 11/03/2020

Federated LQR: Learning through Sharing

In many multi-agent reinforcement learning applications such as flocking...

0 Zhaolin Ren, et al. ∙

research

∙ 08/27/2020

Dynamic Batch Learning in High-Dimensional Sparse Linear Contextual Bandits

We study the problem of dynamic batch learning in high-dimensional spars...

0 Zhimei Ren, et al. ∙

research

∙ 07/11/2020

Federated Learning's Blessing: FedAvg has Linear Speedup

Federated learning (FL) learns a model jointly from a set of participati...

20 Zhaonan Qu, et al. ∙

research

∙ 07/09/2020

Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions

First-price auctions have very recently swept the online advertising ind...

0 Yanjun Han, et al. ∙

research

∙ 06/19/2020

Gradient-free Online Learning in Games with Delayed Rewards

Motivated by applications to online advertising and recommender systems,...

0 Amélie Héliou, et al. ∙

research

∙ 06/10/2020

Distributional Robust Batch Contextual Bandits

Policy learning using historical observational data is an important prob...

17 Nian Si, et al. ∙

research

∙ 04/30/2020

Distributional Soft Actor Critic for Risk Sensitive Learning

Most of reinforcement learning (RL) algorithms aim at maximizing the exp...

7 Xiaoteng Ma, et al. ∙

research

∙ 04/14/2020

Sequential Batch Learning in Finite-Action Linear Contextual Bandits

We study the sequential batch learning problem in linear contextual band...

9 Yanjun Han, et al. ∙

research

∙ 03/22/2020

Optimal No-regret Learning in Repeated First-price Auctions

We study online learning in repeated first-price auctions with censored ...

0 Yanjun Han, et al. ∙

research

∙ 03/17/2020

Diagonal Preconditioning: Theory and Algorithms

Diagonal preconditioning has been a staple technique in optimization and...

5 Zhaonan Qu, et al. ∙

research

∙ 03/17/2020

Multi-action Offline Policy Learning with Bayesian Optimization

We study an offline multi-action policy learning algorithm based on doub...

0 Fang Cai, et al. ∙

research

∙ 03/11/2020

Delay-Adaptive Learning in Generalized Linear Contextual Bandits

In this paper, we consider online learning in generalized linear context...

0 Jose Blanchet, et al. ∙

research

∙ 02/23/2020

Finite-Time Last-Iterate Convergence for Multi-Agent Learning in Games

We consider multi-agent learning via online gradient descent (OGD) in a ...

7 Tianyi Lin, et al. ∙

research

∙ 12/13/2019

Provably Efficient Reinforcement Learning with Aggregated States

We establish that an optimistic variant of Q-learning applied to a finit...

0 Shi Dong, et al. ∙

research

∙ 12/15/2018

Balanced Linear Contextual Bandits

Contextual bandit algorithms are sensitive to the estimation method of t...

0 Maria Dimakopoulou, et al. ∙

research

∙ 10/10/2018

Offline Multi-Action Policy Learning: Generalization and Optimization

In many settings, a decision-maker wishes to learn a rule, or policy, th...

4 Zhengyuan Zhou, et al. ∙

research

∙ 12/14/2017

MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels

Recent studies have discovered that deep networks are capable of memoriz...

0 Lu Jiang, et al. ∙

research

∙ 12/07/2016

An Infinite Dimensional Model for a Many Server Priority Queue

We consider a Markovian many server queueing system in which customers a...

0 Neal Master, et al. ∙

research

∙ 09/26/2016

An Infinite Dimensional Model for A Single Server Priority Queue

We consider a Markovian single server queue in which customers are preem...

0 Neal Master, et al. ∙

Zhengyuan Zhou

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