3 citations · 7 across the 5 of their papers we have counts for
5 papers
Novel Pivoted Cholesky Decompositions for Efficient Gaussian Process Inference
Filip de Roos, Fabio Muratore
The Cholesky decomposition is a fundamental tool for solving linear systems with symmetric and positive definite matrices which are ubiquitous in linear algebra, optimization, and…
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Filip de Roos, Carl Jidling, Adrian Wills +2
Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms. We provide a probabilistic moti…
High-Dimensional Gaussian Process Inference with Derivatives
Filip de Roos, Alexandra Gessner, Philipp Hennig
Although it is widely known that Gaussian processes can be conditioned on observations of the gradient, this functionality is of limited use due to the prohibitive computational co…
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
Filip de Roos, Philipp Hennig
Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not kno…
Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning
Filip de Roos, Philipp Hennig
Solving symmetric positive definite linear problems is a fundamental computational task in machine learning. The exact solution, famously, is cubicly expensive in the size of the m…