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Found 28 papers, 16 papers with code
Joint Composite Latent Space Bayesian Optimization
To effectively tackle these challenges, we introduce Joint Composite Latent Space Bayesian Optimization (JoCo), a novel framework that jointly trains neural network encoders and probabilistic models to adaptively compress high-dimensional input and output spaces into manageable latent representations.
Unexpected Improvements to Expected Improvement for Bayesian Optimization
Expected Improvement (EI) is arguably the most popular acquisition function in Bayesian optimization and has found countless successful applications, but its performance is often exceeded by that of more recent methods.
Practical Policy Optimization with Personalized Experimentation
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment.
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision maker's latent utility function using preference feedback.
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
We use Bayesian Optimization (BO) and propose a novel surrogate modeling approach for efficiently handling a large number of binary and categorical parameters.
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
We prove that under suitable reparameterizations, the BO policy that maximizes the probabilistic objective is the same as that which maximizes the AF, and therefore, PR enjoys the same regret bounds as the original BO policy using the underlying AF.
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
We consider Bayesian optimization of expensive-to-evaluate experiments that generate vector-valued outcomes over which a decision-maker (DM) has preferences.
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
We demonstrate a clear benefit to using this new class of acquisition functions on benchmark problems, and on a challenging real-world task of estimating a high-dimensional contrast sensitivity function.
Sparse Bayesian Optimization
Bayesian optimization (BO) is a powerful approach to sample-efficient optimization of black-box objective functions.
Robust Multi-Objective Bayesian Optimization Under Input Noise
In many manufacturing processes, the design parameters are subject to random input noise, resulting in a product that is often less performant than expected.
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs.
Interpretable Personalized Experimentation
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments.
Looper: An end-to-end ML platform for product decisions
Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems.
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces
Many real world scientific and industrial applications require optimizing multiple competing black-box objectives.
Bayesian Optimization with High-Dimensional Outputs
However, the Gaussian Process (GP) models typically used as probabilistic surrogates for multi-task Bayesian Optimization scale poorly with the number of outcomes, greatly limiting applicability.
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement
We argue that, even in the noiseless setting, generating multiple candidates in parallel is an incarnation of EHVI with uncertainty in the Pareto frontier and therefore can be addressed using the same underlying technique.
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization
Contextual policies are used in many settings to customize system parameters and actions to the specifics of a particular setting.
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
We propose a novel imitation-learning-based algorithm that distills a TS policy into an explicit policy representation by performing posterior inference and optimization offline.
Real-world Video Adaptation with Reinforcement Learning
Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE).
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
In many real-world scenarios, decision makers seek to efficiently optimize multiple competing objectives in a sample-efficient fashion.
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization
We show empirically that properly addressing these issues significantly improves the efficacy of linear embeddings for BO on a range of problems, including learning a gait policy for robot locomotion.
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Real-world applications frequently have constraints with respect to a currently deployed policy.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
Bayesian Optimization for Policy Search via Online-Offline Experimentation
To alleviate these constraints, we augment online experiments with an offline simulator and apply multi-task Bayesian optimization to tune live machine learning systems.
Practical Transfer Learning for Bayesian Optimization
When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset.
Constrained Bayesian Optimization with Noisy Experiments
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems.
Bias and high-dimensional adjustment in observational studies of peer effects
Naive observational estimators overstate peer effects by 320% and commonly used variables (e. g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%.
Social Influence in Social Advertising: Evidence from Field Experiments
This approach can increase ad efficacy for two main reasons: peers' affiliations reflect unobserved consumer characteristics, which are correlated along the social network; and the inclusion of social cues (i. e., peers' association with a brand) alongside ads affect responses via social influence processes.
Social and Information Networks Physics and Society Applications J.4; H.1.2
