Minimax Regret

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6 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Latest papers 27

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m-Set Adversarial Bandits with Winner Feedback
Oct 7, 2026Nicolò Cesa-Bianchi, Matteo PapiniMulti-Armed Bandits Adversarial Bandits
Universit`a degli Studi di Milano · Politecnico di Milano
Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness
Oct 1, 2026Junhyuk Huh, Seoungbin Bae, Dabeen LeeMulti-Armed Bandits Minimax Regret
Faculty of Mathematics, University of Cambridge · Department of Industrial & Systems Engineering, KAIST · Department of Mathematical Sciences, Seoul National University
Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity
Sep 29, 2026Kaixuan Ji, Qiwei Di, Qingyue Zhao +2Multi-Armed Bandits Minimax Regret
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
Prediction with Expert Advice: Anytime Regret with Many Experts Matches the Fixed-Time Constant
Sep 23, 2026Yang Cai, Vineet Gupta, Yanchen Jiang +4Minimax Regret Regret Minimization
Google Research · Yale University · Google DeepMind
Minimax-Optimal Online Contract Design with Unrestricted Bounded Contracts
Sep 17, 2026Rui Ai, David Simchi-Levi, Han ZhongMechanism Design Minimax Regret
Massachusetts Institute of Technology · Shanghai Jiao Tong University
Bandits with Probing: Optimal Regret and the Limits of Winner Feedback
Sep 14, 2026Yongjie GuanMulti-Armed Bandits Minimax Regret
Zhejiang University of Technology
Nearly Minimax-Optimal Regret for Linear Contextual Bandits with Arbitrary Adaptive Action Sets
Sep 14, 2026Tianyuan JinMulti-Armed Bandits Minimax Regret
Data Science and Analytics Thrust The Hong Kong University of Science and Technology (Guangzhou)
Sharp Minimax Regret for Infinite-Memory Logistic Prediction
Aug 27, 2026Vaneet AggarwalLogistic Regression Bayesian Mixture Models
Purdue University
Optimizing Minimax Regret in Uncertain MDPs with Small Sets of Policies
Aug 3, 2026Sterre Lutz, Daniël Vos, Matthijs T. J. Spaan +1Markov Decision Processes Branch and Bound
Delft University of Technology
Parameter-Free Dynamic Regret under Heavy-Tailed Noise
Jul 29, 2026Vaneet AggarwalOnline Convex Optimization Dynamic Regret Minimization
Purdue University
Experimentation and Commitment under Reward Shifts
Jul 26, 2026Puping Jiang, Wei Tang, Renyu +1Multi-Armed Bandits Exploration-Exploitation Tradeoff
Philip
The Price of Hidden Curvature: An $\widetildeΩ (d^{5/4} \sqrt{T})$ Lower Bound for Bandit Convex Optimization
Jul 21, 2026Nived RajaramanOnline Convex Optimization Minimax Regret
Microsoft Research
Min-Max Regret Task Allocation and Planning of Heterogeneous Multi-Robot System in Partially Known Environments
Jul 15, 2026Xinkai Liang, Huixuan Chan, Ying Liu +2Minimax Regret Multi-Robot Exploration
Price of Fairness in Bandits: A Tight Minimax Characterization
Jul 15, 2026Dhruv Sarkar, Soumyadeep Dutta, Sayak Ray ChowdhuryMulti-Armed Bandits Minimax Regret
1Indian Institute of Technology Kharagpur · 2Mohamed bin Zayed University of Artificial Intelligence · 3Indian Institute of Technology Kanpur
Bandit PCA with Minimax Optimal Regret
Jul 12, 2026Moïse Blanchard, Dmitrii Ostrovskii, Aadirupa SahaPrincipal Component Analysis Minimax Regret
Georgia Tech · University of Illinois, Chicago
Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Information
Jul 10, 2026Naman Aggarwal, Jonathan P. HowImperfect-Information Games Game Theory
Aerospace Control Laboratory Laboratory of Information and Decision Systems Massachusetts Institute of Technology
Bellman-sufficient Information Complexity
Jun 9, 2026Yunbei XuMulti-Armed Bandits Minimax Regret
National University of Singapore
Two-Action Apple Tasting with Switching Costs
Jun 2, 2026Tommaso Cesari, Roberto ColomboniMulti-Armed Bandits Minimax Regret
School of Electrical Engineering and Computer Science University of Ottawa Ottawa, Canada · School of Mathematics University of Bristol Bristol, United Kingdom
Minimax-Optimal Policy Regret in Partially Observable Markov Games
Jun 1, 2026Raman AroraRegret Minimization in RL Imperfect-Information Games
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Batched Stochastic Linear Bandits with 1-Bit Communication Constraints
May 29, 2026Ivan Lau, Daniel McMorrow, Kevin Jamieson +1Multi-Armed Bandits Minimax Regret
National University of Singapore · University of Washington
Prudent-Banker: No Extra Fees for Baseline Safety in Adversarial Bandits With and Without Delays
May 22, 2026Ting Hu, Luanda Cai, Emmanouil-Vasileios Vlatakis-GkaragkounisMulti-Armed Bandits Minimax Regret
Department of Finance University of Wisconsin–Madison · Department of Computer Sciences University of Wisconsin–Madison
Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs
May 19, 2026Pierre Boudart, Pierre Gaillard, Alessandro RudiRegret Minimization in RL Markov Decision Processes
SIERRA · INRIA, École Normale Supérieure CNRS, PSL Research University Paris, France · Thoth +3
Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning
May 15, 2026Zijun Chen, Zihan ZhangRegret Minimization in RL Reinforcement Learning
Department of Computer Science and Engineering Hong Kong University of Science and Technology
Profit Maximization in Bilateral Trade against a Smooth Adversary
May 12, 2026Simone Di Gregorio, Paul Dütting, Federico Fusco +1Mechanism Design Minimax Regret
Department of Computer, Control and Management Engineering Sapienza University of Rome · Google Research · Department of Computer Science Aarhus University
Optimal Regret for Single Index Bandits
May 10, 2026Devdan Dey, Sujoy Bhore, Avishek GhoshMulti-Armed Bandits Single-Index Models
Department of Computer Science and Engineering, Indian Institute of Technology Bombay.
Prior-Agnostic Robust Forecast Aggregation
Apr 27, 2026Zhi Chen, Cheng Peng, Wei TangMinimax Regret Probabilistic Forecasting
Chinese University of Hong Kong
Bilateral Trade Under Heavy-Tailed Valuations: Minimax Regret without a Variance Bound
Mar 6, 2026Hangyi ZhaoMinimax Regret Regret Minimization

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

m-Set Adversarial Bandits with Winner Feedback

Nicolò Cesa-Bianchi, Matteo Papini

Universit`a degli Studi di Milano · Politecnico di Milano

Multi-Armed Bandits Adversarial Bandits Combinatorial Multi-Armed Bandits Minimax Regret

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We show upper and lower bounds on the regret of

m

-set adversarial bandits for different utilities (winner reward or sum of rewards) and feedback models (winner index, winner reward, sum of rewards, and their combinations). By comparing to standard bounds for combinatorial and MNL bandits, our results reveal how subtle changes in the setting can have a dramatic impact on the learning rates. Our main technical contributions are the information-theoretic lower bounds on the regret. Experiments on synthetic data confirm our theoretical analyses.

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Latest papers 27