13 citations · 26 across the 13 of their papers we have counts for
13 papers
Computation-Aware Kalman Filtering with Model Selection for Neural Dynamics
JR Huml, Jonathan Wenger, John P. Cunningham
Due to their explicit priors and ability to model uncertainty, Bayesian methods have played a major role in dynamical latent variable modeling of single-cell neural recordings. How…
Variational Deep Learning via Implicit Regularization
Jonathan Wenger, Beau Coker, Juraj Marusic +1
Modern deep learning models generalize remarkably well in-distribution, despite being overparametrized and trained with little to no explicit regularization. Instead, current theor…
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Jonathan Wenger, Kaiwen Wu, Philipp Hennig +3
Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable app…
Computation-Aware Kalman Filtering and Smoothing
Marvin Pförtner, Jonathan Wenger, Jon Cockayne +1
Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and memory complexities scale prohibitively with…
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty
Lukas Tatzel, Jonathan Wenger, Frank Schneider +1
Non-conjugate Gaussian processes (NCGPs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, and are widely used in practice. However, exac…
Large-Scale Gaussian Processes via Alternating Projection
Kaiwen Wu, Jonathan Wenger, Haydn Jones +2
Training and inference in Gaussian processes (GPs) require solving linear systems with kernel matrices. To address the prohibitive time complexity, r…
On the Disconnect Between Theory and Practice of Neural Networks: Limits of the NTK Perspective
Jonathan Wenger, Felix Dangel, Agustinus Kristiadi
The neural tangent kernel (NTK) has garnered significant attention as a theoretical framework for describing the behavior of large-scale neural networks. Kernel methods are theoret…
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
Marvin Pförtner, Ingo Steinwart, Philipp Hennig +1
Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, an…
Posterior and Computational Uncertainty in Gaussian Processes
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner +2
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which inevitably introduce approximation error. Th…
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…
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
Jonathan Wenger, Geoff Pleiss, Philipp Hennig +2
Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, large kernel matrices. Iterative numerical techniques are becoming popular to sca…
Probabilistic Linear Solvers for Machine Learning
Jonathan Wenger, Philipp Hennig
Linear systems are the bedrock of virtually all numerical computation. Machine learning poses specific challenges for the solution of such systems due to their scale, characteristi…
Non-Parametric Calibration for Classification
Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel
Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. However, while many current classification framew…