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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 1151–1200 of 1262 papers

TitleStatusHype
Combinatorial Multi-Armed Bandits with Filtered Feedback—0
Boundary Crossing Probabilities for General Exponential Families—0
Multi-Task Learning for Contextual Bandits—0
Combinatorial Semi-Bandits with Knapsacks—0
Practical Algorithms for Best-K Identification in Multi-Armed Bandits—0
Bandit Regret Scaling with the Effective Loss Range—0
Mostly Exploration-Free Algorithms for Contextual BanditsCode0
Value Directed Exploration in Multi-Armed Bandits with Structured Priors—0
On Kernelized Multi-armed Bandits—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Selective Harvesting over Networks—0
Horde of Bandits using Gaussian Markov Random Fields—0
Contextual Linear Bandits under Noisy Features: Towards Bayesian OraclesCode0
QoS-Aware Multi-Armed Bandits—0
Provably Optimal Algorithms for Generalized Linear Contextual Bandits—0
Rotting Bandits—0
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits—0
Learning to Use Learners' Advice—0
The Price of Differential Privacy For Online Learning—0
Corralling a Band of Bandit AlgorithmsCode0
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits—0
Active Search for Sparse Signals with Region Sensing—0
Multi-armed Bandits: Competing with Optimal Sequences—0
Bandit algorithms to emulate human decision making using probabilistic distortions—0
Fair Algorithms for Infinite and Contextual Bandits—0
Risk-Aware Algorithms for Adversarial Contextual Bandits—0
Exploration Potential—0
On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits—0
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits—0
An optimal learning method for developing personalized treatment regimes—0
Making Contextual Decisions with Low Technical Debt—0
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture—0
Fairness in Learning: Classic and Contextual Bandits—0
Graph Clustering Bandits for Recommendation—0
Stochastic Contextual Bandits with Known Reward Functions—0
Latent Contextual Bandits and their Application to Personalized Recommendations for New Users—0
Cascading Bandits for Large-Scale Recommendation ProblemsCode0
PAC Reinforcement Learning with Rich Observations—0
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits—0
Bandits meet Computer Architecture: Designing a Smartly-allocated Cache—0
Personalized Course Sequence Recommendations—0
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs—0
Algorithms for Differentially Private Multi-Armed Bandits—0
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits—0
Context-Aware Bandits—0
Multi-armed Bandits with Application to 5G Small Cells—0
A Survey of Online Experiment Design with the Stochastic Multi-Armed BanditCode0
Sequential Design for Ranking Response Surfaces—0
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Benchmark Results

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