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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 551–600 of 1262 papers

TitleStatusHype
Access Probability Optimization in RACH: A Multi-Armed Bandits Approach—0
A Bandit Approach to Sequential Experimental Design with False Discovery Control—0
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms—0
Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptions—0
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms—0
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs—0
Convex Hull Monte-Carlo Tree Search—0
Bandits Warm-up Cold Recommender Systems—0
Algorithms for multi-armed bandit problems—0
Continuous-Time Multi-Armed Bandits with Controlled Restarts—0
Continuous K-Max Bandits—0
Bandit Social Learning: Exploration under Myopic Behavior—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making—0
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache—0
Algorithms for Differentially Private Multi-Armed Bandits—0
Contextual Pandora's Box—0
Contextual Online Decision Making with Infinite-Dimensional Functional Regression—0
Bandits for Learning to Explain from Explanations—0
Contextual Multinomial Logit Bandits with General Value Functions—0
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
A KL-LUCB algorithm for Large-Scale Crowdsourcing—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Contextual memory bandit for pro-active dialog engagement—0
Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Contextual Linear Bandits with Delay as Payoff—0
Contextual Information-Directed Sampling—0
Bandit Regret Scaling with the Effective Loss Range—0
A Hybrid Meta-Learning and Multi-Armed Bandit Approach for Context-Specific Multi-Objective Recommendation Optimization—0
Adaptive Data Augmentation for Thompson Sampling—0
A conversion theorem and minimax optimality for continuum contextual bandits—0
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward—0
BanditRank: Learning to Rank Using Contextual Bandits—0
Contextual Combinatorial Conservative Bandits—0
Contextual Causal Bayesian Optimisation—0
BanditQ: Fair Bandits with Guaranteed Rewards—0
A Hierarchical Nearest Neighbour Approach to Contextual Bandits—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
Individual Regret in Cooperative Stochastic Multi-Armed Bandits—0
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits—0
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
Indexability and Rollout Policy for Multi-State Partially Observable Restless Bandits—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Contextual Bandits with Stage-wise Constraints—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Inference for Batched Bandits—0
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems—0
Contextual Bandits with Sparse Data in Web setting—0
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Benchmark Results

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