Batched Multi-armed Bandits Problem
Dec 11, 2019
Speakers
Yanjun Han
Speaker · 1 follower
About
In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for the two-armed stochastic bandits has been completely characterized in \cite{perchet2016batched}, the effect of the number of arms on the regret for the multi-armed case is still open. Moreover, the question whether adaptively chosen batch sizes will help to reduce the regret also remains underexplored. In this paper, we propose the BaSE (batched successive elimination) policy to achieve the rate-optimal regrets (within logarithmic factors) for batched multi-armed bandits, with matching lower bounds even if the batch sizes are determined in an adaptive manner.In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for the two-armed stochastic bandits has been completely characterized in \cite{perchet2016batched}, the effect of the number of arms on the regret for the multi-armed case is still open. Moreover, the question whether adaptively chosen batch sizes will help to reduce the regret also remains underexplored. In this paper,…
Categories
Business & Economics
Category · 3.8k presentations
About NIPS 2019
Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.
Like the format? Trust SlidesLive to capture your next event!
Professional recording and live streaming, delivered globally.
Sharing
Recommended Videos
Presentations on similar topic, category or speaker
Spotlight Talk 2: Flood Detection On Low Cost Orbital Hardware
Watch later
Design and Analysis of Experiments: A Challenge Approach in Teaching
Watch later
Interpretable Comparison of Distributions and Models
Watch later
Guided Similarity Separation for Image Retrieval
Watch later
Contributed talk: Measure Valued Derivatives for Approximate Bayesian Inference
Watch later
