conftrace_

multi-armed bandit

1098 papers

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Also known as

MAB

Co-occurring keywords

regret bound (1926) online learning (1777) stochastic optimization (1060) thompson sampling (237) upper confidence bound (197) regret minimization (315) online algorithm (445) sample complexity (1169) contextual bandit (381) best arm identification (85)

Papers

Kernel Estimation and Model Combination in A Bandit Problem with Covariates JMLR 2016
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models JMLR 2016
Regret of Queueing Bandits NIPS 2016
Conservative Bandits ICML 2016
Multiple-Play Bandits in the Position-Based Model NIPS 2016
A Bandit Framework for Strategic Regression NIPS 2016
Combinatorial semi-bandit with known covariance NIPS 2016
Fairness in Learning: Classic and Contextual Bandits NIPS 2016
Machine Learning in an Auction Environment JMLR 2016
Improved Learning Complexity in Combinatorial Pure Exploration Bandits AISTATS 2016
Anytime optimal algorithms in stochastic multi-armed bandits ICML 2016
Scalable Discrete Sampling as a Multi-Armed Bandit Problem ICML 2016
DCM Bandits: Learning to Rank with Multiple Clicks ICML 2016
An Information-Theoretic Analysis of Thompson Sampling JMLR 2016
Verification Based Solution for Structured MAB Problems NIPS 2016
Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case JMLR 2016
Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture COLT 2016
Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem COLT 2016
Pure Exploration of Multi-armed Bandit Under Matroid Constraints COLT 2016
Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits COLT 2016
Threshold Bandits, With and Without Censored Feedback NIPS 2016
Optimistic Gittins Indices NIPS 2016
Anytime Exploration for Multi-armed Bandits using Confidence Information ICML 2016
PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem ICML 2016
Cascading Bandits: Learning to Rank in the Cascade Model ICML 2015