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Multi-Armed Bandits

1,057 papers

Papers per year

5
27
87
38
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Papers

Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits NIPS 2020
Sub-sampling for Efficient Non-Parametric Bandit Exploration NIPS 2020
Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality NIPS 2020
Bandit Samplers for Training Graph Neural Networks 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
Adversarial Blocking Bandits NIPS 2020
From Finite to Countable-Armed Bandits NIPS 2020
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring NIPS 2020
Bandit Linear Control NIPS 2020
Chaos, Extremism and Optimism: Volume Analysis of Learning in Games 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
Model Selection in Contextual Stochastic Bandit Problems 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
A Bandit Learning Algorithm and Applications to Auction Design NIPS 2020
Locally Differentially Private (Contextual) Bandits Learning NIPS 2020
Latent Bandits Revisited NIPS 2020
Learning Utilities and Equilibria in Non-Truthful Auctions NIPS 2020
Adversarial Attacks on Linear Contextual Bandits NIPS 2020