1 citations · 3 across the 26 of their papers we have counts for
20 papers · 1 filter
Unlearning Offline Stochastic Multi-Armed Bandits
Zichun Ye, Runqi Wang, Xuchuang Wang +3
Machine unlearning aims to unlearn data points from a learned model, offering a principled way to process data-deletion requests and mitigate privacy risks without full retraining.…
Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach
Fatemeh Ghaffari, Siddarth Sitaraman, Xutong Liu +2
Online learning to rank (OLTR) studies how to recommend a short ranked list of items from a large pool and improves future rankings based on user clicks. This setting is commonly m…
Offline Clustering of Preference Learning with Active-data Augmentation
Jingyuan Liu, Fatemeh Ghaffari, Xuchuang Wang +3
Preference learning from pairwise feedback is a widely adopted framework in applications such as reinforcement learning with human feedback and recommendations. In many practical s…
Distributed Algorithms for Multi-Agent Multi-Armed Bandits with Collision
Daoyuan Zhou, Xuchuang Wang, Lin Yang +1
We study the stochastic Multiplayer Multi-Armed Bandit (MMAB) problem, where multiple players select arms to maximize their cumulative rewards. Collisions occur when two or more pl…
Competitive Algorithms for Multi-Agent Ski-Rental Problems
Xuchuang Wang, Bo Sun, Hedyeh Beyhaghi +3
This paper introduces a novel multi-agent ski-rental problem that generalizes the classical ski-rental dilemma to a group setting where agents incur individual and shared costs. In…
Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
Qirun Zeng, Eric He, Richard Hoffmann +2
Adversarial attacks on stochastic bandits have traditionally relied on some unrealistic assumptions, such as per-round reward manipulation and unbounded perturbations, limiting the…
Offline Clustering of Linear Bandits: The Power of Clusters under Limited Data
Jingyuan Liu, Zeyu Zhang, Xuchuang Wang +4
Contextual multi-armed bandit is a fundamental learning framework for making a sequence of decisions, e.g., advertising recommendations for a sequence of arriving users. Recent wor…
Fusing Reward and Dueling Feedback in Stochastic Bandits
Xuchuang Wang, Qirun Zeng, Jinhang Zuo +4
This paper investigates the fusion of absolute (reward) and relative (dueling) feedback in stochastic bandits, where both feedback types are gathered in each decision round. We der…
Heterogeneous Multi-Agent Bandits with Parsimonious Hints
Amirmahdi Mirfakhar, Xuchuang Wang, Jinhang Zuo +2
We study a hinted heterogeneous multi-agent multi-armed bandits problem (HMA2B), where agents can query low-cost observations (hints) in addition to pulling arms. In this framework…
Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models
Mengfan Xu, Liren Shan, Fatemeh Ghaffari +3
We study a novel heterogeneous multi-agent multi-armed bandit problem with a cluster structure induced by stochastic block models, influencing not only graph topology, but also rew…
Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions
Fatemeh Ghaffari, Xuchuang Wang, Jinhang Zuo +1
We study the problem of multi-agent multi-armed bandits with adversarial corruption in a heterogeneous setting, where each agent accesses a subset of arms. The adversary can corrup…
Combinatorial Logistic Bandits
Xutong Liu, Xiangxiang Dai, Xuchuang Wang +2
We introduce a novel framework called combinatorial logistic bandits (CLogB), where in each round, a subset of base arms (called the super arm) is selected, with the outcome of eac…
Stochastic Bandits Robust to Adversarial Attacks
Xuchuang Wang, Jinhang Zuo, Xutong Liu +2
This paper investigates stochastic multi-armed bandit algorithms that are robust to adversarial attacks, where an attacker can first observe the learner's action and {then} alter t…
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
Xutong Liu, Siwei Wang, Jinhang Zuo +7
We introduce a novel framework of combinatorial multi-armed bandits (CMAB) with multivariant and probabilistically triggering arms (CMAB-MT), where the outcome of each arm is a …
Adversarial Attacks on Cooperative Multi-agent Bandits
Jinhang Zuo, Zhiyao Zhang, Xuchuang Wang +5
Cooperative multi-agent multi-armed bandits (CMA2B) consider the collaborative efforts of multiple agents in a shared multi-armed bandit game. We study latent vulnerabilities expos…
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs
Lin Yang, Xuchuang Wang, Mohammad Hajiesmaili +3
Recently, there has been extensive study of cooperative multi-agent multi-armed bandits where a set of distributed agents cooperatively play the same multi-armed bandit game. The g…
Multi-Fidelity Multi-Armed Bandits Revisited
Xuchuang Wang, Qingyun Wu, Wei Chen +1
We study the multi-fidelity multi-armed bandit (MF-MAB), an extension of the canonical multi-armed bandit (MAB) problem. MF-MAB allows each arm to be pulled with different costs (f…
On-Demand Communication for Asynchronous Multi-Agent Bandits
Yu-Zhen Janice Chen, Lin Yang, Xuchuang Wang +4
This paper studies a cooperative multi-agent multi-armed stochastic bandit problem where agents operate asynchronously -- agent pull times and rates are unknown, irregular, and het…
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms
Xuchuang Wang, Hong Xie, John C. S. Lui
We generalize the multiple-play multi-armed bandits (MP-MAB) problem with a shareable arm setting, in which several plays can share the same arm. Furthermore, each shareable arm ha…
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications
Xuchuang Wang, Hong Xie, John C. S. Lui
Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MM…