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03/30/2023
Practical Policy Optimization with Personalized Experimentation
Many organizations measure treatment effects via an experimentation plat...
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03/28/2023
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization
Preferential Bayesian optimization (PBO) is a framework for optimizing a...
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03/03/2023
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
We consider the problem of optimizing expensive black-box functions over...
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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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03/21/2022
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
We consider Bayesian optimization of expensive-to-evaluate experiments t...
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03/18/2022
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
Level set estimation (LSE) is the problem of identifying regions where a...
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03/03/2022
Sparse Bayesian Optimization
Bayesian optimization (BO) is a powerful approach to sample-efficient op...
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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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11/12/2021
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Bayesian optimization (BO) is a sample-efficient approach to optimizing ...
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11/05/2021
Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies
Internet companies are increasingly using machine learning models to cre...
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10/14/2021
Looper: An end-to-end ML platform for product decisions
Modern software systems and products increasingly rely on machine learni...
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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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06/24/2021
Bayesian Optimization with High-Dimensional Outputs
Bayesian Optimization is a sample-efficient black-box optimization proce...
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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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08/28/2020
Real-world Video Adaptation with Reinforcement Learning
Client-side video players employ adaptive bitrate (ABR) algorithms to op...
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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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01/31/2020
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
Bayesian optimization (BO) is a popular approach to optimize expensive-t...
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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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09/30/2019
PlanAlyzer: Assessing Threats to the Validity of Online Experiments
Online experiments are ubiquitous. As the scale of experiments has grown...
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04/29/2019
Shrinkage Estimators in Online Experiments
We develop and analyze empirical Bayes Stein-type estimators for use in ...
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04/01/2019
Bayesian Optimization for Policy Search via Online-Offline Experimentation
Online field experiments are the gold-standard way of evaluating changes...
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02/06/2018
Scalable Meta-Learning for Bayesian Optimization
Bayesian optimization has become a standard technique for hyperparameter...
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06/21/2017
Constrained Bayesian Optimization with Noisy Experiments
Randomized experiments are the gold standard for evaluating the effects ...
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06/14/2017