214 citations · 1.8k across the 88 of their papers we have counts for
11 papers · 1 filter
BackPACK: Packing more into backprop
Felix Dangel, Frederik Kunstner, Philipp Hennig
Automatic differentiation frameworks are optimized for exactly one thing: computing the average mini-batch gradient. Yet, other quantities such as the variance of the mini-batch gr…
Conjugate Gradients for Kernel Machines
Simon Bartels, Philipp Hennig
Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solut…
Integrals over Gaussians under Linear Domain Constraints
Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig
Integrals of linearly constrained multivariate Gaussian densities are a frequent problem in machine learning and statistics, arising in tasks like generalized linear models and Bay…
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Alonso Marco, Dominik Baumann, Philipp Hennig +1
Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging. It is very often the case that some roll…
Uncertainty Estimates for Ordinal Embeddings
Michael Lohaus, Philipp Hennig, Ulrike von Luxburg
To investigate objects without a describable notion of distance, one can gather ordinal information by asking triplet comparisons of the form "Is object closer to or is …
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent
Frederik Kunstner, Lukas Balles, Philipp Hennig
Natural gradient descent, which preconditions a gradient descent update with the Fisher information matrix of the underlying statistical model, is a way to capture partial second-o…