Bandit Learning

Latest papers

57 papers12 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

Instance-dependent sequential limits as an exact information ledger
Oct 30, 2026Akshay BalsubramaniBandit Learning Information-Theoretic Lower Bounds
Conversational Generalized Linear Bandits with Hybrid Feedback
Oct 26, 2026Yurui Zou, Qirun Zeng, Jinhang ZuoBandit Learning Contextual Bandits
University of Science and Technology of China, Hefei, China · City University of Hong Kong, Hong Kong SAR, China
Cascading Bandits with Noisy Observations: Algorithms and Regret Bounds
Oct 21, 2026Yashaswini Kaluguri, Nikhil KaramchandaniMulti-Armed Bandits Bandit Learning
Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India
When Does Momentum Help in Reinforcement Learning with Verifiable Rewards?
Oct 14, 2026Kaifei Wang, Zikang Shan, Liwei Wang +2Bandit Learning Momentum Methods
Peking University · Shanghai Jiao Tong University, SIMIS, and Stanford University · Shanghai Jiao Tong University
Reliable Scale-Free Bandits via Empirical Likelihood
Oct 13, 2026Pratham Gupta, Alhad Sethi, Shubhada AgrawalMulti-Armed Bandits Bandit Learning
Indian Institute of Science
Decentralized Bandit Learning of Markov Perfect Equilibria
Oct 9, 2026Felix Höfer, H. Mete SonerBandit Learning Nash Equilibrium
Booth School of Business, The University of Chicago, Chicago, IL, 60637, USA · Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ, 08540, USA
m-Set Adversarial Bandits with Winner Feedback
Oct 7, 2026Nicolò Cesa-Bianchi, Matteo PapiniMulti-Armed Bandits Bandit Learning
Universit`a degli Studi di Milano · Politecnico di Milano
Beyond Reward Suppression: Near-Optimal Offline Attacks on Warm-Start Bandits with Bounded Rewards
Oct 7, 2026Qirun Zeng, Manhin Poon, Xiangxiang Dai +2Adversarial Attacks Multi-Armed Bandits
City University of Hong Kong, Hong Kong SAR, China · The Chinese University of Hong Kong, Hong Kong SAR, China · Nanyang Technological University, Singapore
The Deceptive Bandit Problem: Exploratory Coupling and the Fragility of Multi-Agent Learning
Oct 6, 2026Michael Tang, Mahmoud Abdelgalil, Jorge I. PovedaMulti-Agent Bandits Bandit Learning
Electrical and Computer Engineering Department at the University of California, San Diego, La Jolla, CA, USA · Mechanical and Aerospace Engineering Department at the University at Buffalo, State University of New York, Buffalo, NY, USA
On the Computational Tractability of Robust Bandits
Oct 6, 2026Vanessa Kosoy, Vinayak PathakMulti-Armed Bandits Robust Multi-Armed Bandits
Faculty of Mathematics, Technion, Haifa, Israel · CORAL† Computational Rational Agents Laboratory, Delaware, USA.
Fast Last-Iterate Convergence in Zero-Sum Markov Games with Bandit Feedback
Oct 5, 2026Yuheng ZhangLast-Iterate Convergence Reinforcement Learning
University of Illinois Urbana-Champaign
Bellman-Centric Learning: Near-Optimal Regret for Linear Bandits with Memory
Oct 5, 2026Jingyuan Liu, Huiwen JiaMulti-Armed Bandits Non-Stationary Bandits
Department of Industrial Engineering and Operations Research, University of California, Berkeley
Bandits in Prod: Hyperparameter Optimization at Inference Time
Sep 1, 2026Louis Abraham, Tuan-Anh Nguyen, Nicolas DevatineMulti-Armed Bandits Inference-Time Optimization
Tiime Paris, France
Bandit Submodular Maximization under Matroid Constraints: Learning Compressed Exchange Policy
Aug 25, 2026Zongqi Wan, Zhijie ZhangSubmodular Optimization Bandit Learning
Great Bay University · Fuzhou University
Sharp Characterization of Bias in Post-Bandit Inference
Aug 2, 2026Lisu Wang, Yilun Chen, Jiaqi LuMulti-Armed Bandits Algorithmic Bias
School of Data Science, The Chinese University of Hong Kong, Shenzhen · School of Management and Economics, The Chinese University of Hong Kong, Shenzhen
Thompson Sampling Is 2-Competitive for Mistakes
Jul 14, 2026Mark Sellke, Gregory ValiantThompson Sampling Multi-Armed Bandits
Learning from Local Walks on Dynamic Graphs with Bandit Feedback
Jul 12, 2026Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1Multi-Armed Bandits Graph Bandits
Department of Computer Science, University of Colorado, Boulder, CO 80309, USA · INRIA, Paris, France
Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning
Jul 1, 2026Haemin Park, Diego Klabjan, Martin W. Braun +2Class-Imbalanced Learning Deep Learning Optimization
Department of Industrial Engineering & Management Sciences, Northwestern University, Evanston, IL, USA · Intel Corporation, Chandler, AZ, USA
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
Jun 8, 2026Udvas Das, Waris Radji, Debabrota Basu +1Multi-Armed Bandits Non-Stationary Bandits
Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 – CRIStAL F-59000 Lille, France
Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits
Jun 8, 2026Vladimir Braverman, Chen Wang, Liudeng Wang +1Multi-Armed Bandits Best-Arm Identification
Johns Hopkins University · Rensselaer Polytechnic Institute · Texas A&M University
Offline-to-Online Learning in Linear Bandits
Jun 3, 2026Kushagra Chandak, Toshinori Kitamura, Xiaoqi TanMulti-Armed Bandits Contextual Bandits
Department of Computing Science, University of Alberta, Canada
Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits
Jun 1, 2026Pu Wang, Yao-Xiang DingMulti-Armed Bandits Best-Arm Identification
State Key Lab of CAD&CG, Zhejiang University
Fairness in two-player zero-sum games with bandit feedback
May 31, 2026S Akash, Pratik GajaneNash Equilibrium Zero-Sum Games
LatentForce.ai Bengaluru, India · Laboratoire d’Informatique Fondamentale d’Orléans University of Orléans, France
Batched Stochastic Linear Bandits with 1-Bit Communication Constraints
May 29, 2026Ivan Lau, Daniel McMorrow, Kevin Jamieson +1Multi-Armed Bandits Batched Bandits
National University of Singapore · University of Washington
Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback
May 25, 2026Anas Barakat, Andreas Kontogiannis, Vasilis Pollatos +2Online Convex Optimization Gradient Descent
Singapore University of Technology and Design · National Technical University of Athens · Archimedes, Athena Research Center, Greece +3
An Interpretable CF-RL-TOPSIS Fusion Model for Skills-Aware Talent Recommendation
May 22, 2026Özkan CanaySequential Recommendation Multi-Criteria Decision Analysis
Sakarya University, Department of Information Systems and Technologies, Faculty of Computer and Information Sciences, Sakarya, Türkiye
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
Delay-Adaptive Speculation Control for Low-Latency Edge-Cloud LLM Inference
May 17, 2026Kangkang Sun, Jianhua Li, Xiuzhen Chen +2Sequential Decision Making Optimal Stopping
Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China · School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
Delightful Exploration
May 13, 2026Ian OsbandMulti-Armed Bandits Value of Information
Google DeepMind
Online Learning-to-Defer with Varying Experts
May 12, 2026Dang Hoang Duy, Yannis Montreuil, Maxime Meyer +3Learning to Defer Bandit Learning
School of Computing, National University of Singapore, Singapore · IPAL, IRL 2955, Singapore · Department of Mathematics, National University of Singapore, Singapore, 117543 +2

Oct 30, 2026J/K move · Enter open · S save

Instance-dependent sequential limits as an exact information ledger

Akshay Balsubramani

Bandit Learning Information-Theoretic Lower Bounds Best-Arm Identification Sequential Hypothesis Testing Markov Decision Processes

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Instance-dependent limits for bandit regret, sequential tests, best-arm identification and Markov decision processes are proved by one chain. It runs from a divergence decomposition through data processing to a statistic, a testing step that turns a class guarantee into a requirement on the history, and optimization over alternatives and allocations. Only the first step is an identity. This work writes the chain as an exact ledger. By the chain rule, data processing loses exactly a conditional divergence, which vanishes for arms in natural exponential families at the per-arm counts and reward sums. A one-parameter family of martingale identities extends the decomposition to every tilt between two instances. The classical limits follow by relaxations with named defects. All defects but the asymptotic step's can be computed from an algorithm's own runs, the uniform quantification's only when the class guarantee is a number. On seven published sources, from movie ratings to engine replacement, the ledger locates the difference between standard algorithms and the classical limits. For identification among trained models and for Wald's test the largest term is usually the uniform quantification's.