research
∙
09/01/2023
Fast and Regret Optimal Best Arm Identification: Fundamental Limits and Low-Complexity Algorithms
This paper considers a stochastic multi-armed bandit (MAB) problem with ...
share
research
∙
06/01/2023
Reconstructing Graph Diffusion History from a Single Snapshot
Diffusion on graphs is ubiquitous with numerous high-impact applications...
research
∙
05/25/2023
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
This paper considers a class of reinforcement learning problems, which i...
research
∙
03/10/2023
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
We study model-free reinforcement learning (RL) algorithms in episodic n...
research
∙
02/05/2023
Online Nonstochastic Control with Adversarial and Static Constraints
This paper studies online nonstochastic control problems with adversaria...
research
∙
01/26/2023
On the Global Convergence of Risk-Averse Policy Gradient Methods with Dynamic Time-Consistent Risk Measures
Risk-sensitive reinforcement learning (RL) has become a popular tool to ...
research
∙
01/04/2023
Network Utility Maximization with Unknown Utility Functions: A Distributed, Data-Driven Bilevel Optimization Approach
Fair resource allocation is one of the most important topics in communic...
research
∙
12/13/2022
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems
This paper studies a class of multi-agent reinforcement learning (MARL) ...
research
∙
09/02/2022
MaxWeight With Discounted UCB: A Provably Stable Scheduling Policy for Nonstationary Multi-Server Systems With Unknown Statistics
Multi-server queueing systems are widely used models for job scheduling ...
research
∙
06/09/2022
On Low-Complexity Quickest Intervention of Mutated Diffusion Processes Through Local Approximation
We consider the problem of controlling a mutated diffusion process with ...
research
∙
05/27/2022
Will Bilevel Optimizers Benefit from Loops
Bilevel optimization has arisen as a powerful tool for solving a variety...
research
∙
05/26/2022
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
Multi-armed bandit (MAB) is a classic model for understanding the explor...
research
∙
02/16/2022
Large-System Insensitivity of Zero-Waiting Load Balancing Algorithms
This paper studies the sensitivity (or insensitivity) of a class of load...
research
∙
11/13/2021
Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning
Obstacle avoidance for small unmanned aircraft is vital for the safety o...
research
∙
06/03/2021
A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processes
This paper presents the first model-free, simulator-free reinforcement l...
research
∙
02/10/2021
An Efficient Pessimistic-Optimistic Algorithm for Constrained Linear Bandits
This paper considers stochastic linear bandits with general constraints....
research
∙
12/11/2020
Beyond Scaling: Calculable Error Bounds of the Power-of-Two-Choices Mean-Field Model in Heavy-Traffic
This paper provides a recipe for deriving calculable approximation error...
research
∙
10/20/2020
POND: Pessimistic-Optimistic oNline Dispatch
This paper considers constrained online dispatch with unknown arrival, r...
research
∙
10/04/2020
FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
In this paper, we propose a new type of Actor, named forward-looking Act...
research
∙
07/09/2020
Provably-Efficient Double Q-Learning
In this paper, we establish a theoretical comparison between the asympto...
research
∙
07/14/2019
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning
We study two time-scale linear stochastic approximation algorithms, whic...
research
∙
03/04/2019
QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection
This paper combines data-driven and model-driven methods for real-time m...
research
∙
02/03/2019
Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning
We consider the dynamics of a linear stochastic approximation algorithm ...
research
∙
08/06/2018
Heavy-Traffic Insensitive Bounds for Weighted Proportionally Fair Bandwidth Sharing Policies
We consider a connection-level model proposed by Massoulié and Roberts f...
research
∙
03/06/2014
Collaborative Filtering with Information-Rich and Information-Sparse Entities
In this paper, we consider a popular model for collaborative filtering i...
research
∙
10/01/2013