56 citations · 118 across the 11 of their papers we have counts for
12 papers
Bayesian Quadrature
Maren Mahsereci, Toni Karvonen
Bayesian quadrature is a probabilistic, model-based approach to numerical integration, the estimation of intractable integrals, or expectations. Although Bayesian quadrature was po…
A Dictionary of Closed-Form Kernel Mean Embeddings
François-Xavier Briol, Alexandra Gessner, Toni Karvonen +1
Kernel mean embeddings -- integrals of a kernel with respect to a probability distribution -- are essential in Bayesian quadrature, but also widely used in other computational tool…
Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods
Andres Fernandez, Frank Schneider, Maren Mahsereci +1
Recently, it has been observed that when training a deep neural net with SGD, the majority of the loss landscape's curvature quickly concentrates in a tiny *top* eigenspace of the…
Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood
Masha Naslidnyk, Motonobu Kanagawa, Toni Karvonen +1
Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted functi…
ProbNum: Probabilistic Numerics in Python
Jonathan Wenger, Nicholas Krämer, Marvin Pförtner +9
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential…
Invariant Priors for Bayesian Quadrature
Masha Naslidnyk, Javier Gonzalez, Maren Mahsereci
Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task…
Emulation of physical processes with Emukit
Andrei Paleyes, Mark Pullin, Maren Mahsereci +3
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical…
A Fourier State Space Model for Bayesian ODE Filters
Hans Kersting, Maren Mahsereci
Gaussian ODE filtering is a probabilistic numerical method to solve ordinary differential equations (ODEs). It computes a Bayesian posterior over the solution from evaluations of t…
Active Multi-Information Source Bayesian Quadrature
Alexandra Gessner, Javier Gonzalez, Maren Mahsereci
Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes…
Probabilistic Line Searches for Stochastic Optimization
Maren Mahsereci, Philipp Hennig
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far b…
Early Stopping without a Validation Set
Maren Mahsereci, Lukas Balles, Christoph Lassner +1
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good…
Probabilistic Line Searches for Stochastic Optimization
Maren Mahsereci, Philipp Hennig
In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far b…