← Learning Types
Machine Learning › Learning Types ›

Multi-Armed Bandits

1044 directly classified papers

Papers per year

Papers

Off-policy evaluation for slate recommendation NIPS 2017
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem NIPS 2017
Monte-Carlo Tree Search by Best Arm Identification NIPS 2017
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications NIPS 2017
Multi-Task Learning for Contextual Bandits NIPS 2017
Improving the Expected Improvement Algorithm NIPS 2017
Boltzmann Exploration Done Right NIPS 2017
A KL-LUCB algorithm for Large-Scale Crowdsourcing NIPS 2017
Position-based Multiple-play Bandit Problem with Unknown Position Bias NIPS 2017
Dynamic Revenue Sharing NIPS 2017
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe NIPS 2017
Identifying Outlier Arms in Multi-Armed Bandit NIPS 2017
Minimal Exploration in Structured Stochastic Bandits NIPS 2017
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control NIPS 2017
Interactive Submodular Bandit NIPS 2017
On Context-Dependent Clustering of Bandits ICML 2017
Counterfactual Data-Fusion for Online Reinforcement Learners ICML 2017
Thresholding Bandits with Augmented UCB IJCAI 2017
Perishability of Data: Dynamic Pricing under Varying-Coefficient Models JMLR 2017
Provably Optimal Algorithms for Generalized Linear Contextual Bandits ICML 2017
Online Learning to Rank with Top-k Feedback JMLR 2017
Contextual Bandits with Latent Confounders: An NMF Approach AISTATS 2017
Coordinated Versus Decentralized Exploration In Multi-Agent Multi-Armed Bandits IJCAI 2017
Optimal Posted-Price Mechanism in Microtask Crowdsourcing IJCAI 2017
Efficient Benchmarking of NLP APIs using Multi-armed Bandits EACL 2017