Multi-Armed Bandits

Hedging in games: Faster convergence of external and swap regrets NeurIPS 2020 Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards ICML 2020 Incentivized Exploration for Multi-Armed Bandits under Reward Drift AAAI 2020 Inference for Batched Bandits NeurIPS 2020 Information Directed Sampling for Linear Partial Monitoring COLT 2020 Latent Bandits Revisited NeurIPS 2020 Learning Utilities and Equilibria in Non-Truthful Auctions NeurIPS 2020 Learning with Good Feature Representations in Bandits and in RL with a Generative Model ICML 2020 Linear Bandits with Feature Feedback AAAI 2020 Locally Differentially Private (Contextual) Bandits Learning NeurIPS 2020 Model Selection in Contextual Stochastic Bandit Problems NeurIPS 2020 Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients AAAI 2020 Multi-Feedback Bandit Learning with Probabilistic Contexts IJCAI 2020 Multi-Player Bandits: The Adversarial Case JMLR 2020 Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits AAAI 2020 Neural Contextual Bandits with UCB-based Exploration ICML 2020 Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without COLT 2020 Old Dog Learns New Tricks: Randomized UCB for Bandit Problems AISTATS 2020 On Adaptivity in Information-Constrained Online Learning AAAI 2020 Online Bayesian Persuasion NeurIPS 2020 Online Evaluation of Audiences for Targeted Advertising via Bandit Experiments AAAI 2020 Online Learning with Vector Costs and Bandits with Knapsacks COLT 2020 Online Second Price Auction with Semi-Bandit Feedback under the Non-Stationary Setting AAAI 2020 On Regret with Multiple Best Arms NeurIPS 2020 On Thompson Sampling for Smoother-than-Lipschitz Bandits AISTATS 2020