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05/28/2023
Sample Complexity of Variance-reduced Distributionally Robust Q-learning
Dynamic decision making under distributional shifts is of fundamental in...
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03/22/2023
Stochastic Nonsmooth Convex Optimization with Heavy-Tailed Noises
Recently, several studies consider the stochastic optimization problem b...
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02/26/2023
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
We consider a reinforcement learning setting in which the deployment env...
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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...
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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...
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01/27/2023
Single-Trajectory Distributionally Robust Reinforcement Learning
As a framework for sequential decision-making, Reinforcement Learning (R...
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11/05/2022
Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions
With the advent and increasing consolidation of e-commerce, digital adve...
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09/14/2022
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation
Among the reasons hindering reinforcement learning (RL) applications to ...
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09/02/2022
Optimal Diagonal Preconditioning: Theory and Practice
Preconditioning has been a staple technique in optimization and machine ...
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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...
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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...
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02/19/2022
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Off-policy evaluation and learning (OPE/L) use offline observational dat...
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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...
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07/08/2021
Computational Benefits of Intermediate Rewards for Hierarchical Planning
Many hierarchical reinforcement learning (RL) applications have empirica...
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07/06/2021
Distributed stochastic optimization with large delays
One of the most widely used methods for solving large-scale stochastic o...
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05/05/2021
Policy Learning with Adaptively Collected Data
Learning optimal policies from historical data enables the gains from pe...
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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...
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03/07/2021
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities
The optimization problems associated with training generative adversaria...
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02/25/2021
Doubly-Adaptive Thompson Sampling for Multi-Armed and Contextual Bandits
To balance exploration and exploitation, multi-armed bandit algorithms n...
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02/10/2021
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State
We design a simple reinforcement learning agent that, with a specificati...
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11/03/2020
Federated LQR: Learning through Sharing
In many multi-agent reinforcement learning applications such as flocking...
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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...
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07/11/2020
Federated Learning's Blessing: FedAvg has Linear Speedup
Federated learning (FL) learns a model jointly from a set of participati...
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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...
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06/19/2020
Gradient-free Online Learning in Games with Delayed Rewards
Motivated by applications to online advertising and recommender systems,...
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06/10/2020
Distributional Robust Batch Contextual Bandits
Policy learning using historical observational data is an important prob...
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04/30/2020
Distributional Soft Actor Critic for Risk Sensitive Learning
Most of reinforcement learning (RL) algorithms aim at maximizing the exp...
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04/14/2020
Sequential Batch Learning in Finite-Action Linear Contextual Bandits
We study the sequential batch learning problem in linear contextual band...
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03/22/2020
Optimal No-regret Learning in Repeated First-price Auctions
We study online learning in repeated first-price auctions with censored ...
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03/17/2020
Diagonal Preconditioning: Theory and Algorithms
Diagonal preconditioning has been a staple technique in optimization and...
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03/17/2020
Multi-action Offline Policy Learning with Bayesian Optimization
We study an offline multi-action policy learning algorithm based on doub...
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03/11/2020
Delay-Adaptive Learning in Generalized Linear Contextual Bandits
In this paper, we consider online learning in generalized linear context...
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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 ...
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12/13/2019
Provably Efficient Reinforcement Learning with Aggregated States
We establish that an optimistic variant of Q-learning applied to a finit...
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12/15/2018
Balanced Linear Contextual Bandits
Contextual bandit algorithms are sensitive to the estimation method of t...
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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...
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12/14/2017
MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels
Recent studies have discovered that deep networks are capable of memoriz...
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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...
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09/26/2016
