← Learning Types
Machine Learning › Learning Types ›

Multi-Armed Bandits

1044 directly classified papers

Papers per year

Papers

Optimistic Bandit Convex Optimization NIPS 2016
Anytime optimal algorithms in stochastic multi-armed bandits ICML 2016
Online Stochastic Linear Optimization under One-bit Feedback ICML 2016
Anytime Exploration for Multi-armed Bandits using Confidence Information ICML 2016
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm ICML 2016
Contextual Combinatorial Cascading Bandits ICML 2016
Conservative Bandits ICML 2016
Distributed Clustering of Linear Bandits in Peer to Peer Networks ICML 2016
Causal Bandits: Learning Good Interventions via Causal Inference NIPS 2016
Machine Learning in an Auction Environment JMLR 2016
Efficient Learning in Large-Scale Combinatorial Semi-Bandits ICML 2015
Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problem COLT 2015
Contextual Dueling Bandits COLT 2015
Bandit Convex Optimization: \sqrtT Regret in One Dimension COLT 2015
Combinatorial Bandits Revisited NIPS 2015
Combinatorial Cascading Bandits NIPS 2015
Revenue Optimization against Strategic Buyers NIPS 2015
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions NIPS 2015
Fighting Bandits with a New Kind of Smoothness NIPS 2015
Convergence Analysis of Prediction Markets via Randomized Subspace Descent NIPS 2015
The Pareto Regret Frontier for Bandits NIPS 2015
Bandits with Unobserved Confounders: A Causal Approach NIPS 2015
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach NIPS 2015
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation NIPS 2015
Linear Multi-Resource Allocation with Semi-Bandit Feedback NIPS 2015