SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 726–750 of 1262 papers

TitleStatusHype
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