Search Results for author:
Found 7 papers, 3 papers with code
SigOpt Mulch: An Intelligent System for AutoML of Gradient Boosted Trees
In this paper, we present SigOpt Mulch, a model-aware hyperparameter tuning system specifically designed for automated tuning of GBTs that provides two improvements over existing systems.
Achieving Diversity in Objective Space for Sample-efficient Search of Multiobjective Optimization Problems
Efficiently solving multi-objective optimization problems for simulation optimization of important scientific and engineering applications such as materials design is becoming an increasingly important research topic.
Scalable Bayesian Transformed Gaussian Processes
The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem and Oliviera, is a fully Bayesian counterpart to the warped Gaussian process (WGP) and marginalizes out a joint prior over input warping and kernel hyperparameters.
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
Bayesian optimization (BO) is a popular method for optimizing expensive-to-evaluate black-box functions.
Cost-aware Bayesian Optimization
Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible.
Efficient Rollout Strategies for Bayesian Optimization
Non-myopic acquisition functions consider the impact of the next $h$ function evaluations and are typically computed through rollout, in which $h$ steps of BO are simulated.
Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.
