A Continuous Relaxation for Discrete Bayesian Optimization

1 code implementation • 26 Apr 2024 • Richard Michael, Simon Bartels, Miguel González-Duque, Yevgen Zainchkovskyy, Jes Frellsen, Søren Hauberg, Wouter Boomsma

To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization.

Adaptive Cholesky Gaussian Processes

1 code implementation • 22 Feb 2022 • Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg

We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data.

Gaussian Processes

Conjugate Gradients for Kernel Machines

no code implementations • 14 Nov 2019 • Simon Bartels, Philipp Hennig

Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning.

BIG-bench Machine Learning regression

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

1 code implementation • 23 May 2016 • Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter

Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks.

Bayesian Optimization BIG-bench Machine Learning +1