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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 51–75 of 1262 papers

TitleStatusHype
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference—0
Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure—0
Tight Gap-Dependent Memory-Regret Trade-Off for Single-Pass Streaming Stochastic Multi-Armed Bandits—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Semi-Parametric Batched Global Multi-Armed Bandits with Covariates—0
Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits—0
Functional multi-armed bandit and the best function identification problems—0
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal TransportabilityCode0
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models—0
Heterogeneous Multi-Agent Bandits with Parsimonious Hints—0
Multi-agent Multi-armed Bandits with Minimum Reward Guarantee FairnessCode0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Efficient and Optimal Policy Gradient Algorithm for Corrupted Multi-armed Bandits—0
Continuous K-Max Bandits—0
Contextual Linear Bandits with Delay as Payoff—0
Model selection for behavioral learning data and applications to contextual bandits—0
Near-Optimal Private Learning in Linear Contextual Bandits—0
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing—0
Contextual bandits with entropy-based human feedbackCode0
Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models—0
Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits—0
Quantile Multi-Armed Bandits with 1-bit Feedback—0
Towards a Sharp Analysis of Offline Policy Learning for f-Divergence-Regularized Contextual Bandits—0
Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical Feedback—0
From Restless to Contextual: A Thresholding Bandit Approach to Improve Finite-horizon PerformanceCode0
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Benchmark Results

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