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 351–375 of 1262 papers

TitleStatusHype
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
Context-Aware Bandits—0
Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks—0
Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection—0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
Delegating via Quitting Games—0
Designing an Interpretable Interface for Contextual Bandits—0
Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions—0
Meta-Learning Bandit Policies by Gradient Ascent—0
Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards—0
Differentially Private Kernelized Contextual Bandits—0
Differentially Private Multi-Armed Bandits in the Shuffle Model—0
Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?—0
Diffusion Approximations for Thompson Sampling—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Diminishing Exploration: A Minimalist Approach to Piecewise Stationary Multi-Armed Bandits—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Discrete Choice Multi-Armed Bandits—0
Disentangling Exploration from Exploitation—0
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Distributed Differential Privacy in Multi-Armed Bandits—0
Distributed Exploration in Multi-Armed Bandits—0
Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
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Benchmark Results

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