Philipp Hennig

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Entropy Search for Information-Efficient Global Optimization

Philipp Hennig, Christian J. Schuler
Journal of Machine Learning Research
2012

Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function… (More)

The Randomized Dependence Coefficient

David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf
NIPS
2013

We introduce the Randomized Dependence Coefficient (RDC), a measure of nonlinear dependence between random variables of arbitrary dimension based on the Hirschfeld-Gebelein-Rényi Maximum Correlation… (More)

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets

Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter
AISTATS
2017

Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. But it is still costly if… (More)

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

Tom Gunter, Michael A. Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts
NIPS
2014

We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Carlo (MCMC)… (More)

Batch Bayesian Optimization via Local Penalization

Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence
AISTATS
2016

The popularity of Bayesian optimization methods for efficient exploration of parameter spaces has lead to a series of papers applying Gaussian processes as surrogates in the optimization of… (More)

Automatic LQR tuning based on Gaussian process global optimization

Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe
IEEE International Conference on Robotics and…
2016

This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is… (More)

Probabilistic Interpretation of Linear Solvers

Philipp Hennig
SIAM Journal on Optimization
2015

This paper proposes a probabilistic framework for algorithms that iteratively solve unconstrained linear problems Bx = b with positive definite B for x. The goal is to replace the point estimates… (More)

Inference of Cause and Effect with Unsupervised Inverse Regression

Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf
AISTATS
2015

We address the problem of causal discovery in the two-variable case given a sample from their joint distribution. The proposed method is based on a known assumption that, if X → Y (X causes Y ), the… (More)

Probabilistic numerics and uncertainty in computations

Philipp Hennig, Michael A. Osborne, Mark A. Girolami
Proceedings. Mathematical, physical, and…
2015

We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return… (More)

Active Uncertainty Calibration in Bayesian ODE Solvers

There is resurging interest, in statistics and machine learning, in solvers for ordinary differential equations (ODEs) that return probability measures instead of point estimates. Recently, Conrad et… (More)

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