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10/18/2022
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
Optimizing expensive-to-evaluate black-box functions of discrete (and po...
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10/04/2022
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
Gaussian processes (GPs) produce good probabilistic models of functions,...
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02/15/2022
Robust Multi-Objective Bayesian Optimization Under Input Noise
Bayesian optimization (BO) is a sample-efficient approach for tuning des...
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09/22/2021
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
The ability to optimize multiple competing objective functions with high...
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05/17/2021
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
Optimizing multiple competing black-box objectives is a challenging prob...
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11/29/2020
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
Thompson sampling (TS) has emerged as a robust technique for contextual ...
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06/09/2020
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
In many real-world scenarios, decision makers seek to efficiently optimi...
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11/02/2019
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Recent advances in contextual bandit optimization and reinforcement lear...
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10/14/2019
BoTorch: Programmable Bayesian Optimization in PyTorch
Bayesian optimization provides sample-efficient global optimization for ...
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06/20/2017
