Cover Image

Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ghaffari, Fatemeh, Wang, Xuchuang, Zuo, Jinhang, Hajiesmaili, Mohammad
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Stochastic Bandits Robust to Adversarial Attacks
by: Wang, Xuchuang, et al.
Published: (2024)

Online Learning to Rank under Corruption: A Robust Cascading Bandits Approach
by: Ghaffari, Fatemeh, et al.
Published: (2025)

Heterogeneous Multi-Agent Bandits with Parsimonious Hints
by: Mirfakhar, Amirmahdi, et al.
Published: (2025)

Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models
by: Xu, Mengfan, et al.
Published: (2025)

Fusing Reward and Dueling Feedback in Stochastic Bandits
by: Wang, Xuchuang, et al.
Published: (2025)

Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection
by: Zeng, Qirun, et al.
Published: (2025)

Unlearning Offline Stochastic Multi-Armed Bandits
by: Ye, Zichun, et al.
Published: (2026)

Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
by: Liu, Xutong, et al.
Published: (2024)

Offline Clustering of Preference Learning with Active-data Augmentation
by: Liu, Jingyuan, et al.
Published: (2025)

Combinatorial Logistic Bandits
by: Liu, Xutong, et al.
Published: (2024)

Contextual Combinatorial Bandits with Probabilistically Triggered Arms
by: Liu, Xutong, et al.
Published: (2023)

Cascading Bandits Robust to Adversarial Corruptions
by: Xie, Jize, et al.
Published: (2025)

Distributed Algorithms for Multi-Agent Multi-Armed Bandits with Collision
by: Zhou, Daoyuan, et al.
Published: (2025)

Offline Clustering of Linear Bandits: The Power of Clusters under Limited Data
by: Liu, Jingyuan, et al.
Published: (2025)

Competitive Algorithms for Multi-Agent Ski-Rental Problems
by: Wang, Xuchuang, et al.
Published: (2025)

A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyond
by: Hu, Zicheng, et al.
Published: (2025)

Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses
by: Zuo, Shiliang
Published: (2020)

Offline Learning for Combinatorial Multi-armed Bandits
by: Liu, Xutong, et al.
Published: (2025)

Robust Decentralized Multi-armed Bandits: From Corruption-Resilience to Byzantine-Resilience
by: Hu, Zicheng, et al.
Published: (2025)

Stealthy Adversarial Attacks on Stochastic Multi-Armed Bandits
by: Wang, Zhiwei, et al.
Published: (2024)

Robust and Computationally Efficient Linear Contextual Bandits under Adversarial Corruption and Heavy-Tailed Noise
by: Tani, Naoto, et al.
Published: (2026)

Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
by: Oh, Youngmin
Published: (2026)

Corruption-Robust Lipschitz Contextual Search
by: Zuo, Shiliang
Published: (2023)

Online Multi-LLM Selection via Contextual Bandits under Unstructured Context Evolution
by: Poon, Manhin, et al.
Published: (2025)

Efficient and Optimal Policy Gradient Algorithm for Corrupted Multi-armed Bandits
by: Liu, Jiayuan, et al.
Published: (2025)

Robust Learning-Augmented Dictionaries
by: Zeynali, Ali, et al.
Published: (2024)

Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification
by: Liu, Haolin, et al.
Published: (2024)

Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks
by: Magesh, Akshayaa, et al.
Published: (2025)

A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
by: Kim, Sanghwa, et al.
Published: (2026)

Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit
by: Yao, Duanyi, et al.
Published: (2024)

Communication-Corruption Coupling and Verification in Cooperative Multi-Objective Bandits
by: Shi, Ming
Published: (2026)

Learning Best Paths in Quantum Networks
by: Wang, Xuchuang, et al.
Published: (2025)

Robust Distribution Learning with Local and Global Adversarial Corruptions
by: Nietert, Sloan, et al.
Published: (2024)

Adversarial Multi-dueling Bandits
by: Gajane, Pratik
Published: (2024)

Cooperative Multi-Agent Constrained Stochastic Linear Bandits
by: Afsharrad, Amirhossein, et al.
Published: (2024)

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
by: Liu, Xutong, et al.
Published: (2022)

Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
by: Ye, Chenlu, et al.
Published: (2022)

Beyond Primal-Dual Methods in Bandits with Stochastic and Adversarial Constraints
by: Bernasconi, Martino, et al.
Published: (2024)

Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
by: Ye, Chenlu, et al.
Published: (2024)

Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
by: Ito, Shinji, et al.
Published: (2025)