214 citations · 1.8k across the 88 of their papers we have counts for
15 papers · 1 filter
Stable Implementation of Probabilistic ODE Solvers
Nicholas Krämer, Philipp Hennig
Probabilistic solvers for ordinary differential equations (ODEs) provide efficient quantification of numerical uncertainty associated with simulation of dynamical systems. Their co…
Calibrated Adaptive Probabilistic ODE Solvers
Nathanael Bosch, Philipp Hennig, Filip Tronarp
Probabilistic solvers for ordinary differential equations assign a posterior measure to the solution of an initial value problem. The joint covariance of this distribution provides…
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering
Ricky T. Q. Chen, Dami Choi, Lukas Balles +2
Standard first-order stochastic optimization algorithms base their updates solely on the average mini-batch gradient, and it has been shown that tracking additional quantities such…
Probabilistic Linear Solvers for Machine Learning
Jonathan Wenger, Philipp Hennig
Linear systems are the bedrock of virtually all numerical computation. Machine learning poses specific challenges for the solution of such systems due to their scale, characteristi…
Robot Learning with Crash Constraints
Alonso Marco, Dominik Baumann, Majid Khadiv +3
In the past decade, numerous machine learning algorithms have been shown to successfully learn optimal policies to control real robotic systems. However, it is common to encounter…
Learnable Uncertainty under Laplace Approximations
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Laplace approximations are classic, computationally lightweight means for constructing Bayesian neural networks (BNNs). As in other approximate BNNs, one cannot necessarily expect…