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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 1001–1025 of 1262 papers

TitleStatusHype
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewardsCode0
Model selection for contextual banditsCode0
Equipping Experts/Bandits with Long-term Memory—0
Rarely-switching linear bandits: optimization of causal effects for the real world—0
Multi-Objective Generalized Linear Bandits—0
Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits—0
Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?—0
Regret Bounds for Thompson Sampling in Episodic Restless Bandit ProblemsCode0
Top-k Combinatorial Bandits with Full-Bandit Feedback—0
Are sample means in multi-armed bandits positively or negatively biased?—0
Achieving Fairness in Stochastic Multi-armed Bandit Problem—0
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits—0
Data Poisoning Attacks on Stochastic Bandits—0
Lessons from Contextual Bandit Learning in a Customer Support Bot—0
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits—0
Meta-learners' learning dynamics are unlike learners'—0
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without—0
Constrained Restless Bandits for Dynamic Scheduling in Cyber-Physical Systems—0
Introduction to Multi-Armed BanditsCode0
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication—0
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits—0
Batched Multi-armed Bandits ProblemCode0
A Survey on Practical Applications of Multi-Armed and Contextual Bandits—0
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits—0
Meta-Learning surrogate models for sequential decision making—0
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Benchmark Results

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