In this paper, we study the role of feedback in online learning with
swi...
In this paper, we study the problem of (finite horizon tabular) Markov
d...
We study model-free reinforcement learning (RL) algorithms in episodic
n...
We consider cross-silo federated linear contextual bandit (LCB) problem ...
We study private and robust multi-armed bandits (MABs), where the agent
...
In this paper, we study kernelized bandits with distributed biased feedb...
With the blooming of Internet-of-Things (IoT), we are witnessing an expl...
In narrow spaces, motion planning based on the traditional hierarchical
...
In this paper, we study the problem of global reward maximization with o...
To fully utilize the abundant spectrum resources in millimeter wave (mmW...
We study the constrained reinforcement learning problem, in which an age...
We consider the standard K-armed bandit problem under a distributed trus...
This paper investigates the problem of regret minimization in linear
tim...
We propose a deep generative approach to nonparametric estimation of
con...
We study a stochastic bandit problem with a general unknown reward funct...
Differential privacy (DP) has been recently introduced to linear context...
Motivated by the wide adoption of reinforcement learning (RL) in real-wo...
This paper investigates the massive connectivity of low Earth orbit (LEO...
Deep neural networks are vulnerable to adversarial examples, which can f...
We study regret minimization in finite horizon tabular Markov decision
p...
Conditional distribution is a fundamental quantity for describing the
re...
We propose a deep generative approach to sampling from a conditional
dis...
Adversarial attacks are feasible in the real world for object detection....
In this paper, we study the problem of regret minimization in reinforcem...
The flourishing low-Earth orbit (LEO) constellation communication networ...
In this paper, we consider the Gaussian process (GP) bandit optimization...
We introduce Tuna, a static analysis approach to optimizing deep neural
...
In this paper, we consider the time-varying Bayesian optimization proble...
Electric city bus gains popularity in recent years for its low greenhous...
Motivated by the increasing concern about privacy in nowadays data-inten...
Applications in cloud platforms motivate the study of efficient load
bal...
This paper investigates the problem of regret minimization for multi-arm...
In this note, we apply Stein's method to analyze the performance of gene...
In this note, we apply Stein's method to analyze the steady-state
distri...
We consider the load balancing problem in large-scale heterogeneous syst...
In this paper, we consider a load balancing system under a general pull-...
Heavy traffic analysis for load balancing policies has relied heavily on...
We establish a unified analytical framework for load balancing systems, ...