Linear Contextual Bandits

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Nearly Minimax-Optimal Regret for Linear Contextual Bandits with Arbitrary Adaptive Action Sets
Sep 14, 2026Tianyuan JinLinear Contextual Bandits Bandits
When Greedy Sampling Explores: KL-Regularized Contextual Bandits without Eluder-Dimension Dependence
Sep 11, 2026Zichen Wang, Haoyang Hong, Huazheng WangLinear Contextual Bandits Kullback-Leibler Regularization
Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
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Designing for the Next Click: Bandits for Real-Time Page Layout
Aug 30, 2026Bhavtosh Rath, Harshith Narasimhamurthy, Bob Eisinger +3Linear Contextual Bandits Layouts
When Offline Evaluation Misleads: A Diagnostic Protocol for Reward and Policy Selection in Delayed-Feedback Contextual Bandits
Aug 12, 2026Sang Su Lee, Vineeth Loganathan, Shishir Dash +1Linear Contextual Bandits Off-Policy Evaluation
Progressive Content Refinement with Decaying Reward Joint LinUCB
Aug 7, 2026Shion Ishikawa, Pablo Loyola, Young-joo Chung +1Linear Contextual Bandits Refinement
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
Aug 6, 2026Devansh Gupta, Shiv Tavker, Dmitry Efimov +3Linear Contextual Bandits In-Context Learning
Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits
Jul 24, 2026Yuta Natsubori, Masataka Ushiku, Yuta SaitoOff-Policy Evaluation Linear Contextual Bandits
A Predict-then-Correct Loop Based on Few-Shot Continuous Contextual Bandit for Demand Forecasting
Jul 17, 2026Zhiwei Lei, Benedict Jun Ma, Ilya JacksonDemand Forecasting Linear Contextual Bandits
Information-Directed Sampling for Causal Bandits
Jul 17, 2026Muhammad Qasim Elahi, Murat Kocaoglu, Mahsa GhasemiLinear Contextual Bandits Linear Regret
Kernel weighted importance sampling for off-policy evaluation in contextual bandits
Jul 16, 2026Joshua Spear, Matthieu Komorowski, Rebecca Pope +2Linear Contextual Bandits Off-Policy Evaluation
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
Jul 15, 2026Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu +11Agent Memory Large Language Model Agents
A Contextual-Bandit Oversight Game with Two-Sided Informational Asymmetry
Jun 30, 2026Yunjin TongLinear Contextual Bandits Bandits
Contextual Slate GLM Bandits with Limited Adaptivity
Jun 30, 2026Tanmay Goyal, Sukruta Prakash Midigeshi, Gaurav SinhaLinear Contextual Bandits Multi-Armed Bandits
Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces
Jun 26, 2026Joyanta Jyoti Mondal, Ibne Farabi Shihab, Anuj SharmaLinear Contextual Bandits Multi-Armed Bandits
Counterfactual learning of new adaptive instructional policies using logged data
Jun 22, 2026Samuel Girard, Sein Minn, Amel Bouzeghoub +1Adaptive Testing Adaptive Learning
Statistical Inference for Misspecified Contextual Bandits
Jun 21, 2026Yongyi Guo, Ziping XuLinear Contextual Bandits Statistical Inference
AdaPrivate-TS: Private Thompson Sampling for Contextual Bandits with Privacy Amplification
Jun 19, 2026Mohammadreza Riyazat, Eranga UkwattaLinear Contextual Bandits Thompson Sampling
Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret
Jun 8, 2026Seoungbin Bae, Dabeen LeeLinear Contextual Bandits Efficient Exploration
CAAL: Contextual Bandits based Online Hand-Craft Active Learning Strategy Selection
Jun 6, 2026Shao-An Yin, Jiacong Li, Tianpei Xie +3Active Learning Linear Contextual Bandits
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
Jun 4, 2026Haoyang Hong, Zichen Wang, Quanquan Gu +1Kullback-Leibler Regularization Linear Contextual Bandits
Decision-Focused On-Policy Learning for Contextual Linear Optimization with Partial Feedback
May 31, 2026Wyame Benslimane, Tinghan Ye, Pascal Van Hentenryck +1Linear Contextual Bandits Optimal Policies
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
May 31, 2026Sanghoon Yu, Min-hwan OhLinear Contextual Bandits Updates
The Sample Complexity of Multiclass and Sparse Contextual Bandits
May 28, 2026Liad Erez, Fan Chen, Alon Cohen +4Linear Contextual Bandits Sample Complexity
Linear and Neural Dueling Bandits with Delayed Feedback
May 26, 2026Xiangyi Wang, Pingchen Lu, Jie Mao +4Linear Contextual Bandits Linear Bandits
Active Learning for Stochastic Contextual Linear Bandits
May 24, 2026Emma Brunskill, Ishani Karmarkar, Zhaoqi LiLinear Contextual Bandits Active Learning
Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning
May 22, 2026Oleg MiroshnichenkoLinear Contextual Bandits Prices
Offline Contextual Bandits in the Presence of New Actions
May 18, 2026Ren Kishimoto, Tatsuhiro Shimizu, Kazuki Kawamura +6Linear Contextual Bandits Off-Policy Evaluation
Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target
May 17, 2026Taesan Kim, Hyeongjun Yun, Jaegul Choo +1Linear Contextual Bandits Bandits
Pessimistic Risk-Aware Policy Learning in Contextual Bandits
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Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning
May 14, 2026Yingying Fan, Yuxuan Han, Jinchi Lv +2Prices Linear Contextual Bandits
Contextual Bandits for Resource-Constrained Devices using Probabilistic Learning
May 13, 2026Marco Angioli, Kevin Johansson, Antonello Rosato +2Linear Contextual Bandits Resource-Constrained Edge Devices
ReAD: Reinforcement-Guided Capability Distillation for Large Language Models
May 11, 2026Xueqi Cheng, Xugui Zhou, Tyler Derr +1Limited Data Linear Contextual Bandits
Signature Approach for Contextual Bandits with Nonlinear and Path-dependent Rewards
May 11, 2026Xin Guo, Grace He, Xinyu LiLinear Contextual Bandits Bandits
Ensemble Distributionally Robust Bayesian Optimisation with Continuous Context
May 8, 2026Tigran Ramazyan, Denis DerkachBayesian Optimization Robust Optimization
Constrained Contextual Bandits with Adversarial Contexts
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Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity
May 5, 2026Riddhiman Bhattacharyya, Sayak Chakrabarty, Imon BanerjeeMarkov Decision Processes Linear Contextual Bandits
Instance-Level Costs for Nuanced Classifier Evaluation
May 4, 2026Kabir Kang, Stephen MussmannEqual Error Rate Linear Contextual Bandits
Scaling Federated Linear Contextual Bandits via Sketching
May 1, 2026Hantao Yang, Hong Xie, Xutong Liu +1Linear Contextual Bandits Federated Learning
Data Deletion Can Help in Adaptive RL
Apr 30, 2026Param Budhraja, Aditya Gangrade, Alex Olshevsky +1Linear Contextual Bandits Markov Decision Processes
The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
Apr 29, 2026Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas +2Recommender Systems Large-Scale Recommendation Systems
Direction-Aware Offline-to-Online Learning in Linear Contextual Bandits
Apr 27, 2026Zean Han, Ruihan Lin, Zezhen Ding +1Linear Contextual Bandits Near-Optimal Regret Guarantees
Collaborative Contextual Bayesian Optimization
Apr 20, 2026Chih-Yu Chang, Qiyuan Chen, Tianhan Gao +5Bayesian Optimization Linear Contextual Bandits
POLAR: Online Learning for LoRA Adapter Caching and Routing in Edge LLM Serving
Apr 17, 2026Shaoang Li, Jian LiLarge Language Model Routing Low-Rank Adaptation Adapters
Calibration-Gated LLM Pseudo-Observations for Online Contextual Bandits
Apr 16, 2026Maksim Pershin, Ivan Golovanov, Pavel Baltabaev +1Linear Contextual Bandits Counterfactual Reasoning
Learning When to Trust in Contextual Social Bandits
Mar 9, 2026Majid Ghasemi, Mark CrowleyLinear Contextual Bandits Feedback Learning
Taming the Monster Every Context: Complexity Measure and Unified Framework for Offline-Oracle Efficient Contextual Bandits
Feb 10, 2026Hao Qin, Chicheng ZhangLinear Contextual Bandits Online
Learning to Route and Schedule LLMs from User Retrials via Contextual Queueing Bandits
Feb 2, 2026Seoungbin Bae, Junyoung Son, Dabeen LeeLarge Language Model Routing Linear Contextual Bandits
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
Dec 3, 2025Juan Diego Toscano, Daniel T. Chen, George Em KarniadakisModular Agentic Framework Agentic Reasoning
Learning Peer Influence Probabilities with Linear Contextual Bandits
Oct 21, 2025Ahmed Sayeed Faruk, Mohammad Shahverdikondori, Elena ZhelevaLinear Contextual Bandits Influence
Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits
Oct 12, 2025Jiazheng Sun, Weixin Wang, Pan XuLinear Contextual Bandits
Best-of-Both Worlds for linear contextual bandits with paid observations
Oct 8, 2025Nathan Boyer, Dorian Baudry, Patrick RebeschiniLinear Contextual Bandits Partially Observable Markov Decision Process
Primal-dual algorithm for contextual stochastic combinatorial optimization
May 7, 2025Louis Bouvier, Thibault Prunet, Vincent Leclère +1Stochastic Optimization Primal-Dual Methods
The Minimal Search Space for Conditional Causal Bandits
Feb 10, 2025Francisco N. F. Q. Simoes, Itai Feigenbaum, Mehdi Dastani +1Causal Graph Linear Contextual Bandits

Sep 14, 2026 · cs.LGJ/K move · Enter open · S save

Nearly Minimax-Optimal Regret for Linear Contextual Bandits with Arbitrary Adaptive Action Sets

Tianyuan Jin

Data Science and Analytics Thrust The Hong Kong University of Science and Technology (Guangzhou)

We study stochastic linear contextual bandits with arbitrary action menus that may depend on the fixed parameter and the interaction history. We establish matching upper and lower bounds, up to logarithmic factors. Let

d

be the dimension,

K

be the menu size, and

T

the time horizon. For

2\le K\le d

, we prove an upper bound

\widetilde O(K^{1/4}\sqrt{dT})

. When

T\ge d^2

, we further prove a lower bound

Ω(K^{1/4}\sqrt{dT})

. Thus, for

T\ge d^2

and

2\le K\le d

, the upper and lower bounds match up to logarithmic factors, and the polynomial dependence on

K

is optimal. Compared with the previous

\widetilde O(\sqrt{dKT})

bound, our upper bound improves the dependence on

K

by a factor of

K^{1/4}

. For

K\ge d

, we prove an upper bound

\widetilde O_{d,T}\left(\sqrt{dT}\min\{\sqrt d,(d\log K)^{1/4}\}\right)

and a lower bound

Ω\left(\sqrt{dT}\min\left\{\sqrt d,\left(\frac{d\log K}{\log(2d)}\right)^{1/4}\right\}\right)

. Here,

\widetilde O_{d,T}

omits logarithmic factors only in

d

and

T

. In particular, for polynomially large

K\ge d

, the upper and lower bounds both scale as

d^{3/4}\sqrt T

up to logarithmic factors, improving the standard

\widetilde O(d\sqrt T)

rate by a factor of

d^{1/4}

. As

K

grows further, the regret smoothly recovers the

d\sqrt T

scale once

\log K

reaches order

d

Linear Contextual Bandits Bandits Upper Bounds X-Logsmask Polynomials

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