← Learning Types
Machine Learning › Learning Types ›

Multi-Armed Bandits

1044 directly classified papers

Papers per year

Papers

Matching in Multi-arm Bandit with Collision NIPS 2022
Non-stationary Bandits with Knapsacks NIPS 2022
Linear Bandit Algorithms with Sublinear Time Complexity ICML 2022
Thompson Sampling for Robust Transfer in Multi-Task Bandits ICML 2022
Optimal Clustering with Noisy Queries via Multi-Armed Bandit ICML 2022
When Are Linear Stochastic Bandits Attackable? ICML 2022
Fixed-Budget Best-Arm Identification in Structured Bandits IJCAI 2022
Differentially Private Regret Minimization in Episodic Markov Decision Processes AAAI 2022
Model Selection in Batch Policy Optimization ICML 2022
Instance Dependent Regret Analysis of Kernelized Bandits ICML 2022
UniRank: Unimodal Bandit Algorithms for Online Ranking ICML 2022
Supervised Off-Policy Ranking ICML 2022
Smoothed Adversarial Linear Contextual Bandits with Knapsacks ICML 2022
Langevin Monte Carlo for Contextual Bandits ICML 2022
Distributionally-Aware Kernelized Bandit Problems for Risk Aversion ICML 2022
A Lyapunov-Based Methodology for Constrained Optimization with Bandit Feedback AAAI 2022
Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization AAAI 2022
Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms ICML 2022
Safe Exploration for Efficient Policy Evaluation and Comparison ICML 2022
Peer Prediction for Learning Agents NIPS 2022
On Elimination Strategies for Bandit Fixed-Confidence Identification NIPS 2022
Diversified Recommendations for Agents with Adaptive Preferences NIPS 2022
Feature and Parameter Selection in Stochastic Linear Bandits ICML 2022
Stochastic Rising Bandits ICML 2022
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences ICML 2022