:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Zuo, Shiliang
Format:	Preprint
Published:	2020
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2008.09312
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)

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

Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
by: Iwazaki, Shogo
Published: (2026)

Near-Optimal Regret in Adversarial Kernel Bandits
by: Zhang, Yu-Jie, et al.
Published: (2026)

Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions
by: Ghaffari, Fatemeh, et al.
Published: (2024)

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

Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure
by: Slivkins, Aleksandrs, et al.
Published: (2025)

Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
by: Di, Qiwei, et al.
Published: (2024)

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

Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Moments
by: Zuo, Shiliang
Published: (2024)

Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
by: Li, Zhuohua, et al.
Published: (2025)

Near-Optimal Regret for Efficient Stochastic Combinatorial Semi-Bandits
by: Ye, Zichun, et al.
Published: (2025)

Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach
by: Qiu, Hao, et al.
Published: (2026)

On Pareto Optimality for Parametric Choice Bandits
by: Zuo, Jierui, 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)

Harnessing the Continuous Structure: Utilizing the First-order Approach in Online Contract Design
by: Zuo, Shiliang
Published: (2024)

Attack-Resistant Uniform Fairness for Linear and Smooth Contextual Bandits
by: Zhang, Qingwen, et al.
Published: (2026)

Near Optimal Pure Exploration in Logistic Bandits
by: Rivera, Eduardo Ochoa, et al.
Published: (2024)

Adversarial Attacks on Combinatorial Multi-Armed Bandits
by: Balasubramanian, Rishab, et al.
Published: (2023)

Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing
by: Xin, Yuan, et al.
Published: (2023)

Nearly Minimax Optimal Submodular Maximization with Bandit Feedback
by: Tajdini, Artin, et al.
Published: (2023)

Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
by: Iwazaki, Shogo, et al.
Published: (2024)

Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
by: Lee, Joongkyu, et al.
Published: (2024)

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

Efficient Adversarial Attacks on High-dimensional Offline Bandits
by: Hosseini, Seyed Mohammad Hadi, et al.
Published: (2026)

Game Theory for Adversarial Attacks and Defenses
by: Sharma, Shorya
Published: (2021)

Test-Time Defense Against Adversarial Attacks via Stochastic Resonance of Latent Ensembles
by: Lao, Dong, et al.
Published: (2025)

Nearly Optimal Best Arm Identification for Semiparametric Bandits
by: Kim, Seok-Jin
Published: (2026)

A Nearly Optimal Single Loop Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
by: Gong, Xiaochuan, et al.
Published: (2024)

Adversarial Training for Defense Against Label Poisoning Attacks
by: Bal, Melis Ilayda, et al.
Published: (2025)

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

Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
by: Cassel, Asaf, et al.
Published: (2024)

Clus-UCB: A Near-Optimal Algorithm for Clustered Bandits
by: Gore, Aakash, et al.
Published: (2025)

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

Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning
by: Jiang, Jiashuo
Published: (2023)

Near Optimal Best Arm Identification for Clustered Bandits
by: Yash, et al.
Published: (2025)

Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks
by: Saday, Artun, et al.
Published: (2025)

Learning to Attack: A Bandit Approach to Adversarial Context Poisoning
by: Telikani, Ray, et al.
Published: (2026)

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

Deep Adversarial Defense Against Multilevel-Lp Attacks
by: Wang, Ren, et al.
Published: (2024)