Simon Bartels
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View article: A systematic analysis of regression models for protein engineering
A systematic analysis of regression models for protein engineering Open
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimizat…
View article: A Continuous Relaxation for Discrete Bayesian Optimization
A Continuous Relaxation for Discrete Bayesian Optimization Open
To optimize efficiently over discrete data and with only few available target observations is a challenge in Bayesian optimization. We propose a continuous relaxation of the objective function and show that inference and optimization can b…
View article: A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences Open
Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficiency, Bayesian optimization is an ideal candidate for these …
View article: Assessing the performance of protein regression models
Assessing the performance of protein regression models Open
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimizat…
View article: Adaptive Cholesky Gaussian Processes
Adaptive Cholesky Gaussian Processes Open
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with litt…
View article: Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition Open
Algorithms involving Gaussian processes or determinantal point processes typically require computing the determinant of a kernel matrix. Frequently, the latter is computed from the Cholesky decomposition, an algorithm of cubic complexity i…
View article: Probabilistic Linear Algebra
Probabilistic Linear Algebra Open
Linear algebra operations are at the core of many computational tasks. For example, evaluating the density of a multivariate normal distribution requires the solution of a linear equation system and the determinant of a square matrix. Freq…
View article: Conjugate Gradients for Kernel Machines
Conjugate Gradients for Kernel Machines Open
Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solution have cubic complexity in the number of datapoints, larg…
View article: Fast Bayesian hyperparameter optimization on large datasets
Fast Bayesian hyperparameter optimization on large datasets Open
Bayesian optimization has become a successful tool for optimizing the hyperparameters of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating…
View article: Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets Open
Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. Despite its success, for large datasets, training and validating a …