Department News

ERC Starting Grant for Philipp Hennig

  • 11 September 2017

Project Title PANAMA

Probabilistic Automated Numerical Analysis in Machine learning and Artificial intelligence

Philipp Hennig


ICERM Seminar on Probabilistic Scientific Computing

  • 13 December 2016

An upcoming workshop in June 2017 will explore applications of probabilistic numerics.

Philipp Hennig


Dagstuhl Seminar on the Future of Learning with Kernels and Gaussian Processes

  • 03 December 2016

A recent meeting at the Leibniz Centre for Computer Science highlights the ongoing significance of analytic nonparametric models for machine learning.

Philipp Hennig


Summer School on Probabilistic Numerics in 2017

  • 01 November 2016

The Dobbiaco Summer School 2017 will be devoted to Probabilistic Numerics, and taught by Philipp Hennig & Mark Girolami.

Philipp Hennig


NIPS workshop: Optimizing the Optimizer

  • 26 August 2016

contributions invited

Maren Mahsereci and Philipp Hennig are co-organizing a NIPS workshop (with Alex Davies at Google) on parameter inference for nonlinear optimization algorithms.

Maren Mahsereci Philipp Hennig


Setting up the Max Planck Group on Probabilistic Numerics

  • 01 August 2016

Max Planck Society funds focussed research program on uncertainty in computation

Our research group will be funded as an independent entity within the Max Planck Institute for Intelligent Systems from December 2016. An official set-up phase starts in September 2016. This also brings an end to our beloved status as an Emmy Noether group.

Philipp Hennig


NIPS Oral for Maren Mahsereci

  • 01 December 2015

PhD student will present her work on optimization for deep learning

Maren Mahsereci's paper on probabilistic line searches for stochastic optimization has been selected for a full oral presentation at the flagship conference of machine learning.

Maren Mahsereci Philipp Hennig


NIPS Oral for Michael Schober

  • 01 December 2014

PhD student will present his work on probabilistic solvers for differential equations

Michael Schober' paper on probabilistic solvers for ordinary differential equations has been selected for a full oral presentation at the flagship conference of machine learning.

Michael Schober Philipp Hennig