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 301–325 of 1262 papers

TitleStatusHype
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward—0
A conversion theorem and minimax optimality for continuum contextual bandits—0
Contextual Information-Directed Sampling—0
Contextual Linear Bandits with Delay as Payoff—0
Contextual memory bandit for pro-active dialog engagement—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Contextual Multinomial Logit Bandits with General Value Functions—0
Contextual Online Decision Making with Infinite-Dimensional Functional Regression—0
Contextual Pandora's Box—0
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Continuous K-Max Bandits—0
Continuous-Time Multi-Armed Bandits with Controlled Restarts—0
Convex Hull Monte-Carlo Tree Search—0
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs—0
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions—0
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Coordination without communication: optimal regret in two players multi-armed bandits—0
Bandits with Partially Observable Confounded Data—0
CorrAttack: Black-box Adversarial Attack with Structured Search—0
Bandits with Temporal Stochastic Constraints—0
Almost Boltzmann Exploration—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
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Benchmark Results

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