research
∙
06/16/2023
Understanding the Role of Feedback in Online Learning with Switching Costs
In this paper, we study the role of feedback in online learning with swi...
share
research
∙
06/01/2023
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards
In this paper, we study the problem of (finite horizon tabular) Markov d...
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
∙
02/27/2023
On Differentially Private Federated Linear Contextual Bandits
We consider cross-silo federated linear contextual bandit (LCB) problem ...
research
∙
02/06/2023
On Private and Robust Bandits
We study private and robust multi-armed bandits (MABs), where the agent ...
research
∙
01/28/2023
(Private) Kernelized Bandits with Distributed Biased Feedback
In this paper, we study kernelized bandits with distributed biased feedb...
research
∙
09/25/2022
Grant-Free NOMA-OTFS Paradigm: Enabling Efficient Ubiquitous Access for LEO Satellite Internet-of-Things
With the blooming of Internet-of-Things (IoT), we are witnessing an expl...
research
∙
09/17/2022
Reinforcement Learning for Self-exploration in Narrow Spaces
In narrow spaces, motion planning based on the traditional hierarchical ...
research
∙
07/12/2022
Differentially Private Linear Bandits with Partial Distributed Feedback
In this paper, we study the problem of global reward maximization with o...
research
∙
07/02/2022
Interference Constrained Beam Alignment for Time-Varying Channels via Kernelized Bandits
To fully utilize the abundant spectrum resources in millimeter wave (mmW...
research
∙
06/23/2022
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
We study the constrained reinforcement learning problem, in which an age...
research
∙
06/12/2022
Distributed Differential Privacy in Multi-Armed Bandits
We consider the standard K-armed bandit problem under a distributed trus...
research
∙
06/06/2022
Learning to Control under Time-Varying Environment
This paper investigates the problem of regret minimization in linear tim...
research
∙
05/19/2022
Deep Generative Survival Analysis: Nonparametric Estimation of Conditional Survival Function
We propose a deep generative approach to nonparametric estimation of con...
research
∙
03/29/2022
On Kernelized Multi-Armed Bandits with Constraints
We study a stochastic bandit problem with a general unknown reward funct...
research
∙
02/11/2022
Shuffle Private Linear Contextual Bandits
Differential privacy (DP) has been recently introduced to linear context...
research
∙
01/18/2022
Differentially Private Reinforcement Learning with Linear Function Approximation
Motivated by the wide adoption of reinforcement learning (RL) in real-wo...
research
∙
01/06/2022
Active Terminal Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA-OTFS in LEO Satellite Internet-of-Things
This paper investigates the massive connectivity of low Earth orbit (LEO...
research
∙
01/01/2022
Adversarial Attack via Dual-Stage Network Erosion
Deep neural networks are vulnerable to adversarial examples, which can f...
research
∙
12/20/2021
Differentially Private Regret Minimization in Episodic Markov Decision Processes
We study regret minimization in finite horizon tabular Markov decision p...
research
∙
12/19/2021
Wasserstein Generative Learning of Conditional Distribution
Conditional distribution is a fundamental quantity for describing the re...
research
∙
10/19/2021
A Deep Generative Approach to Conditional Sampling
We propose a deep generative approach to sampling from a conditional dis...
research
∙
09/01/2021
DPA: Learning Robust Physical Adversarial Camouflages for Object Detectors
Adversarial attacks are feasible in the real world for object detection....
research
∙
08/26/2021
Adaptive Control of Differentially Private Linear Quadratic Systems
In this paper, we study the problem of regret minimization in reinforcem...
research
∙
08/03/2021
Joint Active User Detection and Channel Estimation for Grant-Free NOMA-OTFS in LEO Constellation Internet-of-Things
The flourishing low-Earth orbit (LEO) constellation communication networ...
research
∙
07/06/2021
Weighted Gaussian Process Bandits for Non-stationary Environments
In this paper, we consider the Gaussian process (GP) bandit optimization...
research
∙
04/29/2021
Tuna: A Static Analysis Approach to Optimizing Deep Neural Networks
We introduce Tuna, a static analysis approach to optimizing deep neural ...
research
∙
02/11/2021
No-Regret Algorithms for Time-Varying Bayesian Optimization
In this paper, we consider the time-varying Bayesian optimization proble...
research
∙
10/28/2020
Cloud-Based Dynamic Programming for an Electric City Bus Energy Management Considering Real-Time Passenger Load Prediction
Electric city bus gains popularity in recent years for its low greenhous...
research
∙
10/13/2020
Local Differential Privacy for Bayesian Optimization
Motivated by the increasing concern about privacy in nowadays data-inten...
research
∙
08/20/2020
Optimal Load Balancing in Bipartite Graphs
Applications in cloud platforms motivate the study of efficient load bal...
research
∙
07/06/2020
Multi-Armed Bandits with Local Differential Privacy
This paper investigates the problem of regret minimization for multi-arm...
research
∙
04/20/2020
A Note on Load Balancing in Many-Server Heavy-Traffic Regime
In this note, we apply Stein's method to analyze the performance of gene...
research
∙
03/13/2020
A Note on Stein's Method for Heavy-Traffic Analysis
In this note, we apply Stein's method to analyze the steady-state distri...
research
∙
02/20/2020
Asymptotically Optimal Load Balancing in Large-scale Heterogeneous Systems with Multiple Dispatchers
We consider the load balancing problem in large-scale heterogeneous syst...
research
∙
08/21/2018
Heavy-traffic Delay Optimality in Pull-based Load Balancing Systems: Necessary and Sufficient Conditions
In this paper, we consider a load balancing system under a general pull-...
research
∙
06/08/2018
Flexible Load Balancing with Multi-dimensional State-space Collapse: Throughput and Heavy-traffic Delay Optimality
Heavy traffic analysis for load balancing policies has relied heavily on...
research
∙
10/12/2017
