2 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
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…
cs.LG2021★ 2 cited
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…
cs.LG2019★ 1 cited
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…