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Multi-Armed Bandits

1044 directly classified papers

Papers per year

Papers

Online Learning with Gaussian Payoffs and Side Observations NIPS 2015
Copeland Dueling Bandits NIPS 2015
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs NIPS 2015
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring NIPS 2015
Explore no more: Improved high-probability regret bounds for non-stochastic bandits NIPS 2015
Budgeted Bandit Problems with Continuous Random Costs ACML 2015
Cheap Bandits ICML 2015
Qualitative Multi-Armed Bandits: A Quantile-Based Approach ICML 2015
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments ICML 2015
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays ICML 2015
Simple regret for infinitely many armed bandits ICML 2015
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback ICML 2015
Efficient learning by implicit exploration in bandit problems with side observations NIPS 2014
Stochastic Regret Minimization via Thompson Sampling COLT 2014
Optimal PAC Multiple Arm Identification with Applications to Crowdsourcing ICML 2014
Lipschitz Bandits: Regret Lower Bound and Optimal Algorithms COLT 2014
Follow the Leader with Dropout Perturbations COLT 2014
Robust Multi-objective Learning with Mentor Feedback COLT 2014
Most Correlated Arms Identification COLT 2014
Online Clustering of Bandits ICML 2014
Learning to Optimize via Information-Directed Sampling NIPS 2014
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards NIPS 2014
Spectral Bandits for Smooth Graph Functions ICML 2014
Boosting with Online Binary Learners for the Multiclass Bandit Problem ICML 2014
Online combinatorial optimization with stochastic decision sets and adversarial losses NIPS 2014