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

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Showing 851–875 of 1262 papers

TitleStatusHype
Multinomial Logit Contextual Bandits: Provable Optimality and Practicality—0
Multi-Objective Generalized Linear Bandits—0
Multi-Player Approaches for Dueling Bandits—0
Multi-Player Bandits: A Trekking Approach—0
Multi-Player Bandits Revisited—0
Multi-Player Bandits Robust to Adversarial Collisions—0
Multiplayer Information Asymmetric Contextual Bandits—0
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions—0
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications—0
Decentralized Heterogeneous Multi-Player Multi-Armed Bandits with Non-Zero Rewards on Collisions—0
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms—0
Multiplier Bootstrap-based Exploration—0
MultiScale Contextual Bandits for Long Term Objectives—0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Multi-Task Learning for Contextual Bandits—0
Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access—0
Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Access—0
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users—0
Nearest Neighbor Search Under Uncertainty—0
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits—0
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions—0
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information—0
Towards a Sharp Analysis of Offline Policy Learning for f-Divergence-Regularized Contextual Bandits—0
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration—0
Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem—0
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Benchmark Results

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