← Learning Types
Machine Learning › Learning Types ›

Multi-Armed Bandits

1044 directly classified papers

Papers per year

Papers

Adversarial Attacks on Linear Contextual Bandits NIPS 2020
Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs NIPS 2020
Collapsing Bandits and Their Application to Public Health Intervention NIPS 2020
Batched Coarse Ranking in Multi-Armed Bandits NIPS 2020
Bandit Linear Control NIPS 2020
From Finite to Countable-Armed Bandits NIPS 2020
Adversarial Blocking Bandits NIPS 2020
Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards NIPS 2020
Bandit Samplers for Training Graph Neural Networks NIPS 2020
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality NIPS 2020
Sub-sampling for Efficient Non-Parametric Bandit Exploration NIPS 2020
Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits NIPS 2020
DORB: Dynamically Optimizing Multiple Rewards with Bandits EMNLP 2020
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling NIPS 2020
Delay and Cooperation in Nonstochastic Linear Bandits NIPS 2020
Efficient Contextual Bandits with Continuous Actions NIPS 2020
Multi-Player Bandits: The Adversarial Case JMLR 2020
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting JMLR 2020
Spectral bandits JMLR 2020
Fatigue-Aware Bandits for Dependent Click Models AAAI 2020
Stochastic Online Learning with Probabilistic Graph Feedback AAAI 2020
Efficient Automatic CASH via Rising Bandits AAAI 2020
Online Bayesian Persuasion NIPS 2020
Efficient and Robust High-Dimensional Linear Contextual Bandits IJCAI 2020
Multi-Feedback Bandit Learning with Probabilistic Contexts IJCAI 2020