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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 701–750 of 1262 papers

TitleStatusHype
Online Meta-Learning in Adversarial Multi-Armed Bandits—0
Online Posterior Sampling with a Diffusion Prior—0
Online Residential Demand Response via Contextual Multi-Armed Bandits—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Online Semi-Supervised Learning with Bandit Feedback—0
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent—0
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
On Minimax Optimal Offline Policy Evaluation—0
On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
On Penalization in Stochastic Multi-armed Bandits—0
On Private and Robust Bandits—0
On Quantum Natural Policy Gradients—0
On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits—0
On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Bandits—0
On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits—0
On Speeding Up Language Model Evaluation—0
On Submodular Contextual Bandits—0
On the bias, risk and consistency of sample means in multi-armed bandits—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits—0
Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits—0
On the Problem of Best Arm Retention—0
Contextual Decision-Making with Knapsacks Beyond the Worst Case—0
On The Statistical Complexity of Offline Decision-Making—0
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs—0
On Universally Optimal Algorithms for A/B Testing—0
Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture—0
Open Problem: Model Selection for Contextual Bandits—0
Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards—0
Optimal Activation of Halting Multi-Armed Bandit Models—0
Optimal Algorithms for Range Searching over Multi-Armed Bandits—0
Optimal Algorithms for Stochastic Contextual Preference Bandits—0
Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards—0
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits—0
Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima—0
Optimal cross-learning for contextual bandits with unknown context distributions—0
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity—0
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks—0
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective—0
Optimal Multi-Objective Best Arm Identification with Fixed Confidence—0
Optimal No-regret Learning in Repeated First-price Auctions—0
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits—0
Optimal Streaming Algorithms for Multi-Armed Bandits—0
Optimistic Information Directed Sampling—0
Optimism in the Face of Ambiguity Principle for Multi-Armed Bandits—0
Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics—0
Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits—0
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Benchmark Results

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