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Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 1151–1200 of 1262 papers

TitleStatusHype
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning—0
Batched Bandits with Crowd Externalities—0
Batched Coarse Ranking in Multi-Armed Bandits—0
Regret Bounds for Batched Bandits—0
Batched Nonparametric Bandits via k-Nearest Neighbor UCB—0
Batched Nonparametric Contextual Bandits—0
Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features—0
Batched Thompson Sampling—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
Towards Bayesian Data Selection—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes—0
Beam Learning -- Using Machine Learning for Finding Beam Directions—0
Be Greedy in Multi-Armed Bandits—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme—0
Best-Arm Identification in Correlated Multi-Armed Bandits—0
Best Arm Identification in Linked Bandits—0
Best arm identification in multi-armed bandits with delayed feedback—0
Best Arm Identification in Restless Markov Multi-Armed Bandits—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
Best Arm Identification under Additive Transfer Bandits—0
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits—0
Best-of-Both-Worlds Linear Contextual Bandits—0
Better Algorithms for Stochastic Bandits with Adversarial Corruptions—0
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits—0
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles—0
Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback—0
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits—0
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits—0
Boltzmann Exploration Done Right—0
Bootstrapping Upper Confidence Bound—0
Boundary Crossing Probabilities for General Exponential Families—0
Bounded Regret for Finitely Parameterized Multi-Armed Bandits—0
Breaking the (1/Δ_2) Barrier: Better Batched Best Arm Identification with Adaptive Grids—0
Breaking the T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Budget-Constrained Multi-Armed Bandits with Multiple Plays—0
Budgeted Combinatorial Multi-Armed Bandits—0
Budgeted Recommendation with Delayed Feedback—0
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens—0
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Byzantine-Resilient Decentralized Multi-Armed Bandits—0
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards—0
Causal Bandits: Online Decision-Making in Endogenous Settings—0
Causal Contextual Bandits with Targeted Interventions—0
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System—0
Censored Semi-Bandits for Resource Allocation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified