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Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning

Dec 08, 2020
Hongseok Namkoong, Samuel Daulton, Eytan Bakshy

* Offline Reinforcement Learning Workshop at Neural Information Processing Systems, 2020 

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Real-world Video Adaptation with Reinforcement Learning

Aug 28, 2020
Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy

* Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th International Conference on Machine Learning, Long Beach, California, USA, 2019 

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Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

Jun 11, 2020
Samuel Daulton, Maximilian Balandat, Eytan Bakshy


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Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Jan 31, 2020
Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy


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Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints

Nov 02, 2019
Samuel Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, Eytan Bakshy

* To appear at NeurIPS 2019, Workshop on Safety and Robustness in Decision Making. 11 pages (including references and appendix) 

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BoTorch: Programmable Bayesian Optimization in PyTorch

Oct 14, 2019
Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy


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Bayesian Optimization for Policy Search via Online-Offline Experimentation

Apr 29, 2019
Benjamin Letham, Eytan Bakshy


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Constrained Bayesian Optimization with Noisy Experiments

Jun 26, 2018
Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy


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Scalable Meta-Learning for Bayesian Optimization

Feb 06, 2018
Matthias Feurer, Benjamin Letham, Eytan Bakshy


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Bias and high-dimensional adjustment in observational studies of peer effects

Jun 14, 2017
Dean Eckles, Eytan Bakshy

* 25 pages, 3 figures, 2 tables; supplementary information as ancillary file 

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