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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–950 of 1262 papers

TitleStatusHype
Convex Hull Monte-Carlo Tree Search—0
Online Residential Demand Response via Contextual Multi-Armed Bandits—0
A Farewell to Arms: Sequential Reward Maximization on a Budget with a Giving Up Option—0
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits—0
Stochastic Linear Contextual Bandits with Diverse Contexts—0
Robustness Guarantees for Mode Estimation with an Application to Bandits—0
Taking a hint: How to leverage loss predictors in contextual bandits?—0
Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits—0
Model Selection in Contextual Stochastic Bandit Problems—0
Bounded Regret for Finitely Parameterized Multi-Armed Bandits—0
Decentralized Multi-player Multi-armed Bandits with No Collision Information—0
Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions—0
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis—0
Bandit Learning with Delayed Impact of Actions—0
The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many ArmsCode0
Survey Bandits with Regret Guarantees—0
Online Learning in Contextual Bandits using Gated Linear Networks—0
Residual Bootstrap Exploration for Bandit Algorithms—0
On conditional versus marginal bias in multi-armed bandits—0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Coordination without communication: optimal regret in two players multi-armed bandits—0
Tight Lower Bounds for Combinatorial Multi-Armed Bandits—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles—0
Adversarial Attacks on Linear Contextual Bandits—0
Inference for Batched Bandits—0
Selfish Robustness and Equilibria in Multi-Player Bandits—0
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity—0
Safe Exploration for Optimizing Contextual BanditsCode0
A Closer Look at Small-loss Bounds for Bandits with Graph Feedback—0
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits—0
Bandits with Knapsacks beyond the Worst-Case—0
Ballooning Multi-Armed Bandits—0
Incentivising Exploration and Recommendations for Contextual Bandits with Payments—0
Exploration Through Bias: Revisiting Biased Maximum Likelihood Estimation in Stochastic Multi-Armed Bandits—0
Gradient-free Online Learning in Continuous Games with Delayed Rewards—0
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits—0
A Modern Introduction to Online LearningCode1
Fair Contextual Multi-Armed Bandits: Theory and Experiments—0
Sublinear Optimal Policy Value Estimation in Contextual Bandits—0
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making—0
Offline Contextual Bandits with High Probability Fairness GuaranteesCode0
Learning in Generalized Linear Contextual Bandits with Stochastic Delays—0
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric—0
Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits—0
Thompson Sampling for Multinomial Logit Contextual BanditsCode0
Contextual Combinatorial Conservative Bandits—0
Automatic Ensemble Learning for Online Influence Maximization—0
Corruption-robust exploration in episodic reinforcement learning—0
Contextual Bandits Evolving Over Finite Time—0
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Benchmark Results

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