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Multi-Armed Bandits

1044 directly classified papers

Papers per year

Papers

Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints IJCAI 2020
Best Arm Identification in Spectral Bandits IJCAI 2020
Bayesian Decision Process for Budget-efficient Crowdsourced Clustering IJCAI 2020
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games NIPS 2020
Model Selection in Contextual Stochastic Bandit Problems NIPS 2020
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition NIPS 2020
Choice Bandits NIPS 2020
On Regret with Multiple Best Arms NIPS 2020
Empirical Likelihood for Contextual Bandits NIPS 2020
Inference for Batched Bandits NIPS 2020
Optimal Best-arm Identification in Linear Bandits NIPS 2020
An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits NIPS 2020
FrugalML: How to use ML Prediction APIs more accurately and cheaply NIPS 2020
Stage-wise Conservative Linear Bandits NIPS 2020
Adapting to Misspecification in Contextual Bandits NIPS 2020
Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits NIPS 2020
Monte-Carlo Tree Search in Continuous Action Spaces with Value Gradients AAAI 2020
On Adaptivity in Information-Constrained Online Learning AAAI 2020
Online Second Price Auction with Semi-Bandit Feedback under the Non-Stationary Setting AAAI 2020
A Near-Optimal Change-Detection Based Algorithm for Piecewise-Stationary Combinatorial Semi-Bandits AAAI 2020
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits AAAI 2020
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory AAAI 2020
Safe Linear Stochastic Bandits AAAI 2020
Hedging in games: Faster convergence of external and swap regrets NIPS 2020
Regret Bound for Safe Gaussian Process Bandit Optimization L4DC 2020