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Multi-Armed Bandits

1044 directly classified papers

Papers per year

Papers

Crowd Learning: Improving Online Decision Making Using Crowdsourced Data IJCAI 2017
A Full Non-Monotonic Transition System for Unrestricted Non-Projective Parsing ACL 2017
DCM Bandits: Learning to Rank with Multiple Clicks ICML 2016
No-Regret Algorithms for Heavy-Tailed Linear Bandits ICML 2016
Learning Algorithms for Second-Price Auctions with Reserve JMLR 2016
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games NIPS 2016
Threshold Bandits, With and Without Censored Feedback NIPS 2016
The Multi-fidelity Multi-armed Bandit NIPS 2016
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution NIPS 2016
Efficient Algorithms for Adversarial Contextual Learning ICML 2016
Refined Lower Bounds for Adversarial Bandits NIPS 2016
Tracking the Best Expert in Non-stationary Stochastic Environments NIPS 2016
Regret of Queueing Bandits NIPS 2016
Online Pricing with Strategic and Patient Buyers NIPS 2016
Optimistic Gittins Indices NIPS 2016
Improved Learning Complexity in Combinatorial Pure Exploration Bandits AISTATS 2016
Pricing a Low-regret Seller ICML 2016
Scalable Discrete Sampling as a Multi-Armed Bandit Problem ICML 2016
Multiple-Play Bandits in the Position-Based Model NIPS 2016
Double Thompson Sampling for Dueling Bandits NIPS 2016
Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits JMLR 2016
Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design ICML 2016
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits ICML 2016
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms JMLR 2016
An Information-Theoretic Analysis of Thompson Sampling JMLR 2016