Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning. - Filip de Roos; Philipp Hennig (CoRR, 2017)

Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization. - Filip de Roos; Philipp Hennig (AISTATS, 2019)

High-Dimensional Gaussian Process Inference with Derivatives. - Filip de Roos; Alexandra Gessner; Philipp Hennig (ICML, 2021)

A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization. - Filip de Roos; Carl Jidling; Adrian Wills; Thomas B. Schön; Philipp Hennig (CoRR, 2021)

Novel Pivoted Cholesky Decompositions for Efficient Gaussian Process Inference. - Filip de Roos; Fabio Muratore (CoRR, 2025)