Linearly constrained Gaussian processes. - Carl Jidling; Niklas Wahlström; Adrian Wills; Thomas B. Schön (NIPS, 2017)

Probabilistic approach to limited-data computed tomography reconstruction. - Zenith Purisha; Carl Jidling; Niklas Wahlström; Simo Särkkä; Thomas B. Schön (CoRR, 2018)

A fast quasi-Newton-type method for large-scale stochastic optimisation. - Adrian Wills; Carl Jidling; Thomas B. Schön (CoRR, 2018)

Evaluating the squared-exponential covariance function in Gaussian processes with integral observations. - Johannes N. Hendriks; Carl Jidling; Adrian Wills; Thomas B. Schön (CoRR, 2018)

Deep kernel learning for integral measurements. - Carl Jidling; Johannes N. Hendriks; Thomas B. Schön; Adrian Wills (CoRR, 2019)

Linearly Constrained Neural Networks. - Johannes N. Hendriks; Carl Jidling; Adrian Wills; Thomas B. Schön (CoRR, 2020)

The effect of interventions on COVID-19. - Kristian Soltesz; Fredrik Gustafsson; Toomas Timpka; Joakim Jaldén; Carl Jidling; Albin Heimerson; Thomas B. Schön; Armin Spreco; Joakim Ekberg; Örjan Dahlström; Fredrik Bagge Carlson; Anna Jöud; et al. (Nat., 2020)

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

Incorporating Sum Constraints into Multitask Gaussian Processes. - Philipp Pilar; Carl Jidling; Thomas B. Schön; Niklas Wahlström (Trans. Mach. Learn. Res., 2022)

Tailoring Gaussian processes and large-scale optimisation. - Carl Jidling (2022)