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Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State
We design a simple reinforcement learning agent that, with a specificati...
02/10/2021 ∙ by Shi Dong, et al. ∙
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Using Undersampling with Ensemble Learning to Identify Factors Contributing to Preterm Birth
In this paper, we propose Ensemble Learning models to identify factors c...
09/23/2020 ∙ by Shi Dong, et al. ∙
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Provably Efficient Reinforcement Learning with Aggregated States
We establish that an optimistic variant of Q-learning applied to a finit...
12/13/2019 ∙ by Shi Dong, et al. ∙
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Summarizing CPU and GPU Design Trends with Product Data
Moore's Law and Dennard Scaling have guided the semiconductor industry f...
11/26/2019 ∙ by Yifan Sun, et al. ∙
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Comments on the Du-Kakade-Wang-Yang Lower Bounds
Du, Kakade, Wang, and Yang recently established intriguing lower bounds ...
11/18/2019 ∙ by Benjamin Van Roy, et al. ∙
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On the Performance of Thompson Sampling on Logistic Bandits
We study the logistic bandit, in which rewards are binary with success p...
05/12/2019 ∙ by Shi Dong, et al. ∙
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MGSim + MGMark: A Framework for Multi-GPU System Research
The rapidly growing popularity and scale of data-parallel workloads dema...
10/15/2018 ∙ by Yifan Sun, et al. ∙
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An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Information-theoretic Bayesian regret bounds of Russo and Van Roy captur...
05/30/2018 ∙ by Shi Dong, et al. ∙
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An Information-Theoretic Analysis of Thompson Sampling for Large Action Spaces
Information-theoretic Bayesian regret bounds of Russo and Van Roy captur...
05/30/2018 ∙ by Shi Dong, et al. ∙
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