Generalized Linear Bandits

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Learning While Scheduling Jobs under Context-Dependent Service Rates: An Anytime Rate-Optimal Algorithm
Oct 5, 2026Seoungbin Bae, Dabeen LeeMulti-Armed Bandits Contextual Bandits
Department of Industrial & Systems Engineering, KAIST · Department of Mathematical Sciences, Seoul National University
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
Posterior Tempering Explains Variance Inflation in Linear and Generalized Linear Thompson Sampling
Sep 2, 2026Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya +1Thompson Sampling Multi-Armed Bandits
Daniels School of Business, Purdue University. · Department of Statistics, University of Wisconsin-Madison. · Department of Statistics, Texas A&M University.
Efficient Online Lexicographic Generalized Low-Rank Matrix Bandits
Aug 5, 2026Bo Xue, Ji Cheng, Haodong Jing +2Multi-Armed Bandits Combinatorial Multi-Armed Bandits
City University of Hong Kong. · Xi’an Jiaotong University. · Northwestern Polytechnical University.
On Non-Stationary Dynamic Pricing: Adaptivity and Optimality
Jul 27, 2026Feiyu Jiang, Zifeng ZhaoNon-Stationary Bandits Dynamic Pricing
School of Management, Fudan University · Mendoza College of Business, University of Notre Dame
Nonlinear Bandit
Jul 8, 2026Tianshuo Zheng, Ting Wu, Zhi-Hua Zhou +1Multi-Armed Bandits Heavy-Tailed Noise
School of Mathematics, Nanjing University, Nanjing, 210093, China · School of Artificial Intelligence, Nanjing University, National Key Laboratory for Novel Software Technology, Nanjing, 210023, China · School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China
Contextual Slate GLM Bandits with Limited Adaptivity
Jun 30, 2026Tanmay Goyal, Sukruta Prakash Midigeshi, Gaurav SinhaMulti-Armed Bandits Contextual Bandits
Microsoft Research India
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
May 31, 2026Sanghoon Yu, Min-hwan OhContextual Bandits Linear Contextual Bandits
Seoul National University, Seoul, Republic of Korea
Variance-sensitive Thompson sampling for generalised linear bandits, revisited
May 29, 2026Tom Perneczky, Marc Abeille, David JanzThompson Sampling Multi-Armed Bandits
University of Oxford · Criteo AI Lab
Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent
May 25, 2026Joongkyu Lee, Min-hwan OhMulti-Armed Bandits Dynamic Regret Minimization
Seoul National University
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
Replicable Bandits with UCB based Exploration
Apr 21, 2026Rohan Deb, Udaya Ghai, Karan Singh +1Multi-Armed Bandits Contextual Bandits
University of Illinois Urbana-Champaign · Amazon · Carnegie Mellon University
A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
Feb 11, 2026Sanghwa Kim, Junghyun Lee, Se-Young YunMulti-Armed Bandits Online Mirror Descent
Kim Jaechul Graduate School of AI, KAIST
Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits
Oct 12, 2025Jiazheng Sun, Weixin Wang, Pan XuEnsemble Learning Contextual Bandits
Duke University

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Learning While Scheduling Jobs under Context-Dependent Service Rates: An Anytime Rate-Optimal Algorithm

Seoungbin Bae, Dabeen Lee

Department of Industrial & Systems Engineering, KAIST · Department of Mathematical Sciences, Seoul National University

Multi-Armed Bandits Contextual Bandits Generalized Linear Bandits

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We study contextual queueing bandits, where a learner schedules jobs while learning unknown service rates modeled by logistic functions of job-server features. Performance is measured by queue length regret, the expected excess queue length at round

t

relative to an oracle that knows the service rates. Existing decaying-regret guarantees either have a suboptimal decay rate or require a known fixed horizon. They also assume context-wise slack and a strictly positive minimum eigenvalue of the feature covariance. In this paper, we propose WISE (Widest Interval Selection with Elimination), achieving rate-optimal

\widetilde{\mathcal O}(t^{-1/2})

queue length regret at every sufficiently large time without knowing the horizon. We assume capacity slack, meaning that expected incoming workload under best-server service is below service capacity, and impose no covariance lower bound. Our analysis uses a workload potential measuring the expected service attempts needed by waiting jobs on their best servers. Its drift on nonempty rounds combines a negative term ensured by capacity slack with errors from suboptimal service choices. Then an elliptical potential count bounds how often WISE selects wide confidence intervals, thereby limiting the number of rounds with large service errors. We also sharpen the arrival-rate dependence of an existing lower bound and make its dependence on feature dimension and server count explicit. We prove another lower bound that quantifies the increase in regret as the normalized capacity slack decreases; to our knowledge, this is the first such lower bound for CQB. Simulations show small regret even when context-wise slack fails.

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