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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 851–900 of 1262 papers

TitleStatusHype
Fair Algorithms for Multi-Agent Multi-Armed Bandits—0
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual BanditsCode0
Robust Multi-Agent Multi-Armed Bandits—0
Multi-Armed Bandits with Local Differential Privacy—0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—0
Continuous-Time Multi-Armed Bandits with Controlled Restarts—0
Offline Contextual Bandits with Overparameterized ModelsCode0
Online learning with Corrupted context: Corrupted Contextual Bandits—0
Approximating a Target Distribution using Weight QueriesCode0
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
Open Problem: Model Selection for Contextual Bandits—0
Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect—0
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance WeightingCode0
Stochastic Bandits with Linear Constraints—0
Constrained regret minimization for multi-criterion multi-armed banditsCode0
Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach—0
Finding All ε-Good Arms in Stochastic BanditsCode0
Non-Stationary Off-Policy Optimization—0
Explicit Best Arm Identification in Linear Bandits Using No-Regret Learners—0
Quantile Multi-Armed Bandits: Optimal Best-Arm Identification and a Differentially Private Scheme—0
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed BanditsCode1
Bandits with Partially Observable Confounded Data—0
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation—0
Efficient Contextual Bandits with Continuous ActionsCode1
Gaussian Gated Linear NetworksCode0
Distributionally Robust Batch Contextual Bandits—0
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition—0
Online Learning in Iterated Prisoner's Dilemma to Mimic Human BehaviorCode0
Meta-Learning Bandit Policies by Gradient Ascent—0
Contextual Bandits with Side-Observations—0
Concurrent Decentralized Channel Allocation and Access Point Selection using Multi-Armed Bandits in multi BSS WLANs—0
(Locally) Differentially Private Combinatorial Semi-Bandits—0
Locally Differentially Private (Contextual) Bandits LearningCode0
To update or not to update? Delayed Nonparametric Bandits with Randomized Allocation—0
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits—0
Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RLCode1
Neural Network Retraining for Model Serving—0
Learning to Rank in the Position Based Model with Bandit Feedback—0
Thompson Sampling for Linearly Constrained BanditsCode0
Sequential Batch Learning in Finite-Action Linear Contextual Bandits—0
Power Constrained BanditsCode0
Exploration with Limited Memory: Streaming Algorithms for Coin Tossing, Noisy Comparisons, and Multi-Armed Bandits—0
Hawkes Process Multi-armed Bandits for Disaster Search and Rescue—0
Hierarchical Adaptive Contextual Bandits for Resource Constraint based RecommendationCode1
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability—0
Optimal No-regret Learning in Repeated First-price Auctions—0
Self-Supervised Contextual Bandits in Computer Vision—0
Learning and Fairness in Energy Harvesting: A Maximin Multi-Armed Bandits Approach—0
Delay-Adaptive Learning in Generalized Linear Contextual Bandits—0
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Benchmark Results

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