Multi-Armed Bandits

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48 papers in the last four weeks, up 109% on the four weeks before. 0.3% of all new papers.

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Latent Order Bandits
May 8, 2026Emil Carlsson, Newton Mwai, Fredrik D. JohanssonMulti-Armed Bandits UCB Algorithms
Sleep Cycle AB · Chalmers University of Technology & University of Gothenburg
Cost-Ordered Feasibility for Multi-Armed Bandits with Cost Subsidy
May 8, 2026Ishank Juneja, Carlee Joe-Wong, Osman YağanMulti-Armed Bandits Robust Multi-Armed Bandits
Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh, PA 15213
Conformal-Style Quantile Analyses for Stochastic Bandits
May 8, 2026Chengyu Du, Mengfan XuMulti-Armed Bandits Conformal Prediction
University of Massachusetts Amherst
Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimization
May 7, 2026John Wang, Mengfan XuMulti-Armed Bandits Multi-Agent System Optimization
University of Massachusetts Amherst
Bandit Learning in General Open Multi-agent Systems
May 7, 2026Mengfan XuMulti-Armed Bandits Non-Stationary Bandits
Mechanical and Industrial Engineering University of Massachusetts Amherst
Sharper Guarantees for Misspecified Kernelized Bandit Optimization
May 7, 2026Davide Maran, Csaba SzepesváriMulti-Armed Bandits Kernel Methods
Politecnico di Milano · University of Alberta
Best Arm Identification in Generalized Linear Bandits via Hybrid Feedback
May 7, 2026Qirun Zeng, Xuchuang Wang, Jiayi Shen +3Multi-Armed Bandits Best-Arm Identification
Department of Computer Science City University of Hong Kong, Hong Kong SAR, China · Manning College of Information & Computer Science University of Massachusetts Amherst, Amherst, MA, USA · School of Management University of Science and Technology of China, Hefei, Anhui, China +3
Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
May 6, 2026Harin Lee, Min-hwan OhMulti-Armed Bandits Regret Minimization in RL
University of Washington · Seoul National University
Differential Privacy in the Extensive-Form Bandit Problem
May 6, 2026Stephen Pasteris, Rahul Savani, Theodore TurocyMulti-Armed Bandits Imperfect-Information Games
The Alan Turing Institute London, United Kingdom · The University of Liverpool Liverpool, United Kingdom · The University of East Anglia Norwich, United Kingdom
Vanishing L2 regularization for the softmax Multi Armed Bandit
May 5, 2026Stefana-Lucia Anita, Gabriel TuriniciMulti-Armed Bandits Policy Gradient
CEREMADE, Universit´e Paris Dauphine - PSL, CNRS, Paris, France · ‘Octav Mayer” Institute of Mathematics of the Romanian Academy, Bd. Carol I 8, Ias¸i 700505, Romania
Bandits on graphs and structures
May 5, 2026Michal ValkoMulti-Armed Bandits Graph Bandits
L’ÉCOLE NORMALE SUPÉRIEURE DE CACHAN
On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
May 4, 2026Kaixuan Ji, Qiwei Di, Heyang Zhao +2Multi-Armed Bandits Sample Complexity
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
May 3, 2026Maheed H. Ahmed, Mahsa GhasemiPairwise Preference Learning Multi-Armed Bandits
Electrical and Computer Engineering Purdue University
Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
May 3, 2026Youngmin OhMulti-Armed Bandits Bandits with Delayed Feedback
InfiniTree, Republic of Korea
Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values
May 1, 2026Shradha Sharma, Swapnil Dhamal, Shweta JainMulti-Armed Bandits Cooperative Game Theory
Indian Institute of Technology Ropar
Unlearning Offline Stochastic Multi-Armed Bandits
May 1, 2026Zichun Ye, Runqi Wang, Xuchuang Wang +3Multi-Armed Bandits Robust Multi-Armed Bandits
Shanghai Jiao Tong University, Shanghai, China · University of Massachusetts Amherst, Massachusetts, United States · University of Washington, Tacoma, United States
Revealing graph bandits for maximizing local influence
May 1, 2026Alexandra Carpentier, Michal ValkoMulti-Armed Bandits Graph Bandits
Universität Potsdam · SequeL team, Inria Lille - Nord Europe
Trading off rewards and errors in multi-armed bandits
May 1, 2026Akram Erraqabi, Alessandro Lazaric, Michal Valko +2Multi-Armed Bandits Exploration-Exploitation Tradeoff
Inria SequeL · CMU · EnLearn
Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
Apr 28, 2026Abhirami PillaiMulti-Armed Bandits Causal Effect Estimation
utdallas
Spectral bandits
Apr 28, 2026Tomáš Kocák, Rémi Munos, Branislav Kveton +2Multi-Armed Bandits Graph Bandits
ENS de Lyon, 15 Parvis Ren´e Descartes, 69342 Lyon, France · Inria Lille – Nord Europe, SequeL team · DeepMind Paris, 14 Rue de Londres, 75009 Paris, France +2
Online learning with Erdős-Rényi side-observation graphs
Apr 28, 2026Tomáš Kocák, Gergely Neu, Michal ValkoMulti-Armed Bandits Adversarial Bandits
SequeL team INRIA Lille - Nord Europe · Universitat Pompeu Fabra Barcelona, Spain
Online combinatorial optimization with stochastic decision sets and adversarial losses
Apr 28, 2026Gergely Neu, Michal ValkoMulti-Armed Bandits Exploration-Exploitation Tradeoff
SequeL team, INRIA Lille – Nord Europe, France
Efficient learning by implicit exploration in bandit problems with side observations
Apr 27, 2026Tomas Kocak, Gergely Neu, Michal Valko +1Multi-Armed Bandits Exploration-Exploitation Tradeoff
SequeL team, INRIA Lille – Nord Europe, France · Current affiliation: Google DeepMind
Extreme bandits
Apr 27, 2026Alexandra Carpentier, Michal ValkoMulti-Armed Bandits Exploration-Exploitation Tradeoff
Statistical Laboratory, CMS University of Cambridge, UK · SequeL team INRIA Lille - Nord Europe, France
Stochastic simultaneous optimistic optimization
Apr 27, 2026Michal Valko, Alexandra Carpentier, Rémi MunosStochastic Optimization Multi-Armed Bandits
INRIA Lille - Nord Europe, SequeL team, 40 avenue Halley 59650, Villeneuve d’Ascq, France · Statistical Laboratory, CMS, Wilberforce Road, CB3 0WB, University of Cambridge, United Kingdom
A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks
Apr 27, 2026Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos TserpesMulti-Armed Bandits Dynamic Neural Networks
Department of Electrical and Computer Engineering National Technical University of Athens, Greece
Logistic Bandits with $\tilde{O}(\sqrt{dT})$ Regret without Context Diversity Assumptions
Apr 24, 2026Seoungbin Bae, Dabeen LeeMulti-Armed Bandits Contextual Bandits
Department of Industrial & Systems Engineering, KAIST · Department of Mathematical Sciences, Seoul National University
Concave Statistical Utility Maximization Bandits via Influence-Function Gradients
Apr 24, 2026Matías Carrasco, Alejandro CholaquidisMulti-Armed Bandits Mirror Descent
Universidad ORT Uruguay Montevideo, Uruguay · Facultad de Ciencias Universidad de la República Montevideo, Uruguay
A single algorithm for both restless and rested rotting bandits
Apr 23, 2026Julien Seznec, Pierre Ménard, Alessandro Lazaric +1Multi-Armed Bandits Non-Stationary Bandits
SCOOL, Inria Lille · FAIR Paris · DeepMind Paris
Online Survival Analysis: A Bandit Approach under Cox PH Model
Apr 22, 2026Yang Xu, Wenbin Lu, Rui SongMulti-Armed Bandits Survival Analysis
Department of Statistics · North Carolina State University

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Latent Order Bandits

Emil Carlsson, Newton Mwai, Fredrik D. Johansson

Sleep Cycle AB · Chalmers University of Technology & University of Gothenburg

Multi-Armed Bandits UCB Algorithms

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Bandit algorithms solve diverse sequential decision-making problems, but are often too sample-inefficient for from-scratch personalization. To substantially reduce exploration times, latent bandit algorithms exploit cross-instance structure implied by discrete latent states, provided that the posterior distribution of rewards and latent states is known and accurate. However, obtaining an accurate model of this structure is difficult, and a small number of latent states may be insufficient to characterize the reward distributions in all problem instances. We propose latent order bandits (LOB), relaxing the assumptions of latent bandits to require only prior knowledge of a partial order of action preferences in each state. This allows instances of the same state to vary in reward distributions, as long as the partial order of actions is shared. For example, groups of users on a streaming service may agree on which movie genres are the best but rate experiences on different scales. We give an upper-confidence bound procedure for the LOB problem, applicable to both total and partial latent orders, and give an upper bound on its regret. To improve empirical performance, we propose a posterior-sampling algorithm and show, in a suite of experiments, that both are competitive with full-prior latent bandits when same-state instances share reward parameters, and preferable to them when reward scales differ between instances with the same latent state.