3 citations · 6 across the 22 of their papers we have counts for
23 papers
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.…
Two-Sided Time-Independent Regret for Matching Markets with Limited Interviews
Amirmahdi Mirfakhar, Xuchuang Wang, Mengfan Xu +2
Two-sided matching platforms rely on preferences from both sides, yet participants can evaluate only a small fraction of potential partners. In practice, they use low-cost pre-matc…
The Secretary Problem with Predictions and a Chosen Order
Helia Karisani, Mohammadreza Daneshvaramoli, Hedyeh Beyhaghi +2
We study a learning-augmented variant of the secretary problem, recently introduced by Fujii and Yoshida (2023), in which the decision-maker has access to machine-learned predictio…
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…
Quantum Network Tomography for General Topology with SPAM Errors
Xuchuang Wang, Matheus Guedes De Andrade, Guus Avis +3
The goal of quantum network tomography (QNT) is the characterization of internal quantum channels in a quantum network from external peripheral operations. Prior research has prima…
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…
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…
Learning Best Paths in Quantum Networks
Xuchuang Wang, Maoli Liu, Xutong Liu +4
Quantum networks (QNs) transmit delicate quantum information across noisy quantum channels. Crucial applications, like quantum key distribution (QKD) and distributed quantum comput…
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…
Optimal Online Probe Allocation for Classical and Quantum Network Tomography
Xuchuang Wang, Yu-Zhen Janice Chen, Matheus Guedes de Andrade +4
How to efficiently perform network tomography is a fundamental problem in network management and monitoring. A network tomography task usually consists of applying multiple probing…
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…
No-regret Algorithms for Fair Resource Allocation
Abhishek Sinha, Ativ Joshi, Rajarshi Bhattacharjee +2
We consider a fair resource allocation problem in the no-regret setting against an unrestricted adversary. The objective is to allocate resources equitably among several agents in…
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
Xutong Liu, Jinhang Zuo, Siwei Wang +4
We study contextual combinatorial bandits with probabilistically triggered arms (CMAB-T) under a variety of smoothness conditions that capture a wide range of applications, suc…
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…
Distributed Bandits with Heterogeneous Agents
Lin Yang, Yu-zhen Janice Chen, Mohammad Hajiesmaili +2
This paper tackles a multi-agent bandit setting where agents cooperate together to solve the same instance of a -armed stochastic bandit problem. The agents are \textit{hete…