
Conjugate Gradients for Kernel Machines
Regularized leastsquares (kernelridge / Gaussian process) regression i...
11/14/2019 ∙ by Simon Bartels, et al. ∙ 41 ∙ shareread it

Probabilistic Solutions To Ordinary Differential Equations As NonLinear Bayesian Filtering: A New Perspective
We formulate probabilistic numerical approximations to solutions of ordi...
10/08/2018 ∙ by Filip Tronarp, et al. ∙ 10 ∙ shareread it

Uncertainty Estimates for Ordinal Embeddings
To investigate objects without a describable notion of distance, one can...
06/27/2019 ∙ by Michael Lohaus, et al. ∙ 5 ∙ shareread it

Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Learning robot controllers by minimizing a blackbox objective cost usin...
07/24/2019 ∙ by Alonso Marco, et al. ∙ 3 ∙ shareread it

On the Design of LQR Kernels for Efficient Controller Learning
Finding optimal feedback controllers for nonlinear dynamic systems from ...
09/20/2017 ∙ by Alonso Marco, et al. ∙ 0 ∙ shareread it

Probabilistic Active Learning of Functions in Structural Causal Models
We consider the problem of learning the functions computing children fro...
06/30/2017 ∙ by Paul K. Rubenstein, et al. ∙ 0 ∙ shareread it

Krylov Subspace Recycling for Fast Iterative LeastSquares in Machine Learning
Solving symmetric positive definite linear problems is a fundamental com...
06/01/2017 ∙ by Filip de Roos, et al. ∙ 0 ∙ shareread it

Follow the Signs for Robust Stochastic Optimization
Stochastic noise on gradients is now a common feature in machine learnin...
05/22/2017 ∙ by Lukas Balles, et al. ∙ 0 ∙ shareread it

Probabilistic Line Searches for Stochastic Optimization
In deterministic optimization, line searches are a standard tool ensurin...
03/29/2017 ∙ by Maren Mahsereci, et al. ∙ 0 ∙ shareread it

Early Stopping without a Validation Set
Early stopping is a widely used technique to prevent poor generalization...
03/28/2017 ∙ by Maren Mahsereci, et al. ∙ 0 ∙ shareread it

A probabilistic model for the numerical solution of initial value problems
Like many numerical methods, solvers for initial value problems (IVPs) o...
10/17/2016 ∙ by Michael Schober, et al. ∙ 0 ∙ shareread it

Exact Sampling from Determinantal Point Processes
Determinantal point processes (DPPs) are an important concept in random ...
09/22/2016 ∙ by Philipp Hennig, et al. ∙ 0 ∙ shareread it

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Bayesian optimization has become a successful tool for hyperparameter op...
05/23/2016 ∙ by Aaron Klein, et al. ∙ 0 ∙ shareread it

Coupling Adaptive Batch Sizes with Learning Rates
Minibatch stochastic gradient descent and variants thereof have become ...
12/15/2016 ∙ by Lukas Balles, et al. ∙ 0 ∙ shareread it

Active Uncertainty Calibration in Bayesian ODE Solvers
There is resurging interest, in statistics and machine learning, in solv...
05/11/2016 ∙ by Hans Kersting, et al. ∙ 0 ∙ shareread it

Dual Control for Approximate Bayesian Reinforcement Learning
Control of nonepisodic, finitehorizon dynamical systems with uncertain...
10/13/2015 ∙ by Edgar D. Klenske, et al. ∙ 0 ∙ shareread it

Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithm...
06/03/2015 ∙ by Philipp Hennig, et al. ∙ 0 ∙ shareread it

Batch Bayesian Optimization via Local Penalization
The popularity of Bayesian optimization methods for efficient exploratio...
05/29/2015 ∙ by Javier Gonzalez, et al. ∙ 0 ∙ shareread it

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
We propose a novel sampling framework for inference in probabilistic mod...
11/03/2014 ∙ by Tom Gunter, et al. ∙ 0 ∙ shareread it

Probabilistic Interpretation of Linear Solvers
This manuscript proposes a probabilistic framework for algorithms that i...
02/10/2014 ∙ by Philipp Hennig, et al. ∙ 0 ∙ shareread it

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
We study a probabilistic numerical method for the solution of both bound...
06/03/2013 ∙ by Philipp Hennig, et al. ∙ 0 ∙ shareread it

The Randomized Dependence Coefficient
We introduce the Randomized Dependence Coefficient (RDC), a measure of n...
04/29/2013 ∙ by David LopezPaz, et al. ∙ 0 ∙ shareread it

Entropy Search for InformationEfficient Global Optimization
Contemporary global optimization algorithms are based on local measures ...
12/06/2011 ∙ by Philipp Hennig, et al. ∙ 0 ∙ shareread it

Gaussian Probabilities and Expectation Propagation
While Gaussian probability densities are omnipresent in applied mathemat...
11/29/2011 ∙ by John P. Cunningham, et al. ∙ 0 ∙ shareread it

Kernel Topic Models
Latent Dirichlet Allocation models discrete data as a mixture of discret...
10/21/2011 ∙ by Philipp Hennig, et al. ∙ 0 ∙ shareread it

Optimal Reinforcement Learning for Gaussian Systems
The explorationexploitation tradeoff is among the central challenges o...
06/04/2011 ∙ by Philipp Hennig, et al. ∙ 0 ∙ shareread it

Expectation Propagation on the Maximum of Correlated Normal Variables
Many inference problems involving questions of optimality ask for the ma...
10/01/2009 ∙ by Philipp Hennig, et al. ∙ 0 ∙ shareread it

Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
This paper is an attempt to bridge the conceptual gaps between researche...
07/06/2018 ∙ by Motonobu Kanagawa, et al. ∙ 0 ∙ shareread it

Bayesian Filtering for ODEs with Bounded Derivatives
Recently there has been increasing interest in probabilistic solvers for...
09/25/2017 ∙ by Emilia Magnani, et al. ∙ 0 ∙ shareread it

Probabilistic Linear Solvers: A Unifying View
Several recent works have developed a new, probabilistic interpretation ...
10/08/2018 ∙ by Simon Bartels, et al. ∙ 0 ∙ shareread it

Fast and Robust Shortest Paths on Manifolds Learned from Data
We propose a fast, simple and robust algorithm for computing shortest pa...
01/22/2019 ∙ by Georgios Arvanitidis, et al. ∙ 0 ∙ shareread it

Convergence Rates of Gaussian ODE Filters
A recentlyintroduced class of probabilistic (uncertaintyaware) solvers...
07/25/2018 ∙ by Hans Kersting, et al. ∙ 0 ∙ shareread it

A Modular Approach to Blockdiagonal Hessian Approximations for Secondorder Optimization Methods
We propose a modular extension of the backpropagation algorithm for comp...
02/05/2019 ∙ by Felix Dangel, et al. ∙ 0 ∙ shareread it

Active Probabilistic Inference on Matrices for PreConditioning in Stochastic Optimization
Preconditioning is a wellknown concept that can significantly improve ...
02/20/2019 ∙ by Filip de Roos, et al. ∙ 0 ∙ shareread it

DeepOBS: A Deep Learning Optimizer Benchmark Suite
Because the choice and tuning of the optimizer affects the speed, and ul...
03/13/2019 ∙ by Frank Schneider, et al. ∙ 0 ∙ shareread it

Limitations of the Empirical Fisher Approximation
Natural gradient descent, which preconditions a gradient descent update ...
05/29/2019 ∙ by Frederik Kunstner, et al. ∙ 0 ∙ shareread it

Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical i...
05/24/2019 ∙ by Motonobu Kanagawa, et al. ∙ 0 ∙ shareread it

Integrals over Gaussians under Linear Domain Constraints
Integrals of linearly constrained multivariate Gaussian densities are a ...
10/21/2019 ∙ by Alexandra Gessner, et al. ∙ 0 ∙ shareread it
Philipp Hennig
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Max Planck Research Group Leader at Max Planck Institute for Intelligent Systems