Bandits

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Bandits

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Multi-Armed Bandits

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Linear Bandits

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Generalized Linear Bandit

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Optimal Bandit Algorithms

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Online Learning-to-Defer with Varying Experts
May 12, 2026Dang Hoang Duy, Yannis Montreuil, Maxime Meyer +3Learning-Augmented Algorithms Experts
Multi-Task Representation Learning for Conservative Linear Bandits
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Valid Best-Model Identification for LLM Evaluation via Low-Rank Factorization
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Signature Approach for Contextual Bandits with Nonlinear and Path-dependent Rewards
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Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
May 11, 2026Shogo IwazakiGeneralized Linear Bandit Bandits
Sample-Mean Anchored Thompson Sampling for Offline-to-Online Learning with Distribution Shift
May 11, 2026Bochao Li, Yao Fu, Wei Chen +1Thompson Sampling Learning-Augmented Algorithms
Optimal Regret for Single Index Bandits
May 10, 2026Devdan Dey, Sujoy Bhore, Avishek GhoshMulti-Armed Bandits Regret
Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions
May 10, 2026Soumita Hait, Ping Li, Haipeng Luo +1Imperfect-Information Games Bandits
Worst-Case Regret Bounds for Combinatorial Thompson Sampling in Sleeping Semi-Bandits
May 10, 2026Zhiming Huang, Bingshan Hu, Jianping PanThompson Sampling Linear Bandits
Beyond Static Bias: Adaptive Multi-Fidelity Bandits with Improving Proxies
May 8, 2026Muyun Lu, Haoyang Hong, Huazheng Wang +1Multi-Armed Bandits Generalized Linear Bandit
Multi-Armed Bandits With Best-Action Queries
May 8, 2026Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi +1Multi-Armed Bandits O(T^Β)$ Simultaneous Regret
Latent Order Bandits
May 8, 2026Emil Carlsson, Newton Mwai, Fredrik D. JohanssonOptimal Bandit Algorithms Order
Cost-Ordered Feasibility for Multi-Armed Bandits with Cost Subsidy
May 8, 2026Ishank Juneja, Carlee Joe-Wong, Osman YağanMulti-Armed Bandits Linear Regret
Conformal-Style Quantile Analyses for Stochastic Bandits
May 8, 2026Chengyu Du, Mengfan XuLinear Bandits Quantile Regression
Bandit Learning in General Open Multi-agent Systems
May 7, 2026Mengfan XuLinear Bandits Bandits
Constrained Contextual Bandits with Adversarial Contexts
May 7, 2026Dhruv Sarkar, Abhishek SinhaLinear Contextual Bandits Optimal Bandit Algorithms
Sharper Guarantees for Misspecified Kernelized Bandit Optimization
May 7, 2026Davide Maran, Csaba SzepesváriKernel Method Bandits
Best Arm Identification in Generalized Linear Bandits via Hybrid Feedback
May 7, 2026Qirun Zeng, Xuchuang Wang, Jiayi Shen +3Generalized Linear Bandit Feedback Learning
Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
May 6, 2026Harin Lee, Min-hwan OhLinear Regret Multi-Armed Bandits
On-line Learning in Tree MDPs by Treating Policies as Bandit Arms
May 6, 2026Anvay Shah, Ramsundar Anandanarayanan, Sharayu Moharir +1Markov Decision Processes Bandits
Vanishing L2 regularization for the softmax Multi Armed Bandit
May 5, 2026Stefana-Lucia Anita, Gabriel TuriniciMulti-Armed Bandits Policy Gradient
Bandits attack function optimization
May 5, 2026Philippe Preux, Rémi Munos, Michal ValkoBandits Objective Functions
Bandits on graphs and structures
May 5, 2026Michal ValkoLinear Bandits Bandits
On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
May 4, 2026Kaixuan Ji, Qiwei Di, Heyang Zhao +2Kullback-Leibler Regularization Linear Bandits
Multi-User Dueling Bandits: A Fair Approach using Nash Social Welfare
May 3, 2026Maheed H. Ahmed, Mahsa GhasemiBandits
Robust Linear Dueling Bandits with Post-serving Context under Unknown Delays and Adversarial Corruptions
May 3, 2026Youngmin OhBandits Delay
AI Alignment via Incentives and Correction
May 2, 2026Rohit Agarwal, Joshua Lin, Mark Braverman +1Artificial Intelligence Alignment Incentives
Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values
May 1, 2026Shradha Sharma, Swapnil Dhamal, Shweta JainShapley Value Multi-Armed Bandits
NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
May 1, 2026Sizhe Tang, Zuyuan Zhang, Mahdi Imani +1Monte Carlo Tree Search Autonomous Exploration Strategies
Reinforcement Learning with Markov Risk Measures and Multipattern Risk Approximation
May 1, 2026Andrzej Ruszczynski, Tiangang ZhangQ-Learning Multi-Armed Bandits
Unlearning Offline Stochastic Multi-Armed Bandits
May 1, 2026Zichun Ye, Runqi Wang, Xuchuang Wang +3Unlearning Aims Multi-Armed Bandits
Revealing graph bandits for maximizing local influence
May 1, 2026Alexandra Carpentier, Michal ValkoEfficient Influence Function Bandits
Trading off rewards and errors in multi-armed bandits
May 1, 2026Akram Erraqabi, Alessandro Lazaric, Michal Valko +2Multi-Armed Bandits Regret
TypeBandit: Type-Level Context Allocation and Reweighting for Effective Attribute Completion in Heterogeneous Graph Neural Networks
Apr 30, 2026Ta-Yang Wang, Rajgopal Kannan, Viktor PrasannaText-Attributed Graph Graph Neural Networks
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
Spectral bandits
Apr 28, 2026Tomáš Kocák, Rémi Munos, Branislav Kveton +2Multi-Armed Bandits Bandits
Online learning with Erdős-Rényi side-observation graphs
Apr 28, 2026Tomáš Kocák, Gergely Neu, Michal ValkoBandits Optimal Sample Complexity
Online combinatorial optimization with stochastic decision sets and adversarial losses
Apr 28, 2026Gergely Neu, Michal ValkoBandits Combinatorial Optimization
Co-Director: Agentic Generative Video Storytelling
Apr 27, 2026Yale Song, Yiwen Song, Nick Losier +13Co-Director Visual Storytelling
Efficient learning by implicit exploration in bandit problems with side observations
Apr 27, 2026Tomas Kocak, Gergely Neu, Michal Valko +1Near-Optimal Regret Guarantees Bandits
GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation
Apr 27, 2026Steven Szachara, Sheeraja Rajakrishnan, Dylan Jay Van Allen +3Quantum Error Correction Quantum Circuit Simulation
Extreme bandits
Apr 27, 2026Alexandra Carpentier, Michal ValkoBandits Extremes
A Comparative Analysis on the Performance of Upper Confidence Bound Algorithms in Adaptive Deep Neural Networks
Apr 27, 2026Grigorios Papanikolaou, Ioannis Kontopoulos, Konstantinos TserpesModern Deep Networks Edge Computing
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
Logistic Bandits with $\tilde{O}(\sqrt{dT})$ Regret without Context Diversity Assumptions
Apr 24, 2026Seoungbin Bae, Dabeen LeeO(T^Β)$ Simultaneous Regret Multi-Armed Bandits
A-IC3: Learning-Guided Adaptive Inductive Generalization for Hardware Model Checking
Apr 23, 2026Xiaofeng Zhou, Guangyu Hu, Hongce Zhang +1Probabilistic Model Checking Verifier
A single algorithm for both restless and rested rotting bandits
Apr 23, 2026Julien Seznec, Pierre Ménard, Alessandro Lazaric +1Bandits Non-Stationarity
Adaptive Instruction Composition for Automated LLM Red-Teaming
Apr 22, 2026Jesse Zymet, Andy Luo, Swapnil Shinde +2Red-Teaming Language Model Evasion Attacks
Online Survival Analysis: A Bandit Approach under Cox PH Model
Apr 22, 2026Yang Xu, Wenbin Lu, Rui SongSurvival Analysis Time-To-Event Data
Replicable Bandits with UCB based Exploration
Apr 21, 2026Rohan Deb, Udaya Ghai, Karan Singh +1Bandits Replication
Optimal Exploration of New Products under Assortment Decisions
Apr 20, 2026Jackie Baek, Atanas Dinev, Thodoris LykourisLinear Bandits Products
Spectral bandits for smooth graph functions
Apr 20, 2026Michal Valko, Rémi Munos, Branislav Kveton +1Multi-Armed Bandits Bandits
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
Apr 17, 2026Côme Fiegel, Pierre Ménard, Tadashi Kozuno +2Imperfect-Information Games Mirror Descent
Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier
Apr 16, 2026Come Fiegel, Pierre Menard, Tadashi Kozuno +2Imperfect-Information Games Mirror Descent
Regret Tail Characterization of Optimal Bandit Algorithms with Generic Rewards
Apr 16, 2026Subhodip Panda, Shubhada AgrawalOptimal Bandit Algorithms Regret
Best of both worlds: Stochastic & adversarial best-arm identification
Apr 16, 2026Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon +2Bandits Stochastic
Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems
Mar 5, 2026Tomoki Yamagami, Mikio Hasegawa, Takatomo Mihana +2Linear Bandits Autocorrelations
A Jointly Efficient and Optimal Algorithm for Heteroskedastic Generalized Linear Bandits with Adversarial Corruptions
Feb 11, 2026Sanghwa Kim, Junghyun Lee, Se-Young YunLinear Bandits Adversarial Corruption
Hybrid Feedback-Guided Optimal Learning for Wireless Interactive Panoramic Scene Delivery
Feb 6, 2026Xiaoyi Wu, Juaren Steiger, Bin Li +1Virtual Reality Panoramic Images
Improved Algorithms for Nash Welfare in Linear Bandits
Jan 30, 2026Dhruv Sarkar, Nishant Pandey, Sayak Ray ChowdhuryLinear Bandits Optimal Bandit Algorithms

May 12, 2026 · stat.MLJ/K move · Enter open · S save

Online Learning-to-Defer with Varying Experts

Dang Hoang Duy, Yannis Montreuil, Maxime Meyer, Axel Carlier+2

School of Computing, National University of Singapore, Singapore · IPAL, IRL 2955, Singapore · Department of Mathematics, National University of Singapore, Singapore, 117543 · F´ed´eration ENAC ISAE-SUPAERO ONERA, Universit´e de Toulouse, France · Institute for Infocomm Research, A*STAR, Singapore

Learning-to-Defer (L2D) methods route each query either to a predictive model or to external experts. While existing work studies this problem in batch settings, real-world deployments require handling streaming data, changing expert availability, and shifting expert distribution. We introduce the first online L2D algorithm for multiclass classification with bandit feedback and a dynamically varying pool of experts. Our method achieves regret guarantees of

O((n+n_e)T^{2/3})

in general and

O((n+n_e)\sqrt{T})

under a low-noise condition, where

T

is the time horizon,

n

is the number of labels, and

n_e

is the number of distinct experts observed across rounds. The analysis builds on novel

\mathcal{H}

-consistency bounds for the online framework, combined with first-order methods for online convex optimization. Experiments on synthetic and real-world datasets demonstrate that our approach effectively extends standard Learning-to-Defer to settings with varying expert availability and reliability.

Learning-Augmented Algorithms Experts Expert Demonstrations Bandits Convex Optimization

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