multi-armed bandit

DCM Bandits: Learning to Rank with Multiple Clicks ICML 2016 Double Thompson Sampling for Dueling Bandits NeurIPS 2016 Fairness in Learning: Classic and Contextual Bandits NeurIPS 2016 Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case JMLR 2016 Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations NeurIPS 2016 Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits JMLR 2016 Improved Learning Complexity in Combinatorial Pure Exploration Bandits AISTATS 2016 Kernel Estimation and Model Combination in A Bandit Problem with Covariates JMLR 2016 Machine Learning in an Auction Environment JMLR 2016 Maximin Action Identification: A New Bandit Framework for Games COLT 2016 Multi-armed Bandits: Competing with Optimal Sequences NeurIPS 2016 Multi-Player Bandits – a Musical Chairs Approach ICML 2016 Multiple-Play Bandits in the Position-Based Model NeurIPS 2016 Non-stochastic Best Arm Identification and Hyperparameter Optimization AISTATS 2016 On Explore-Then-Commit strategies NeurIPS 2016 Online Learning with Feedback Graphs Without the Graphs ICML 2016 On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models JMLR 2016 Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture COLT 2016 Optimal Best Arm Identification with Fixed Confidence COLT 2016 Optimistic Gittins Indices NeurIPS 2016 PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem ICML 2016 Pricing a Low-regret Seller ICML 2016 Pure Exploration of Multi-armed Bandit Under Matroid Constraints COLT 2016 Refined Lower Bounds for Adversarial Bandits NeurIPS 2016 Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits COLT 2016