215 citations · 613 across the 19 of their papers we have counts for
10 papers · 1 filter
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
William Herlands, Daniel B. Neill, Hannes Nickisch +1
Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unid…
State Space Gaussian Processes with Non-Gaussian Likelihood
Hannes Nickisch, Arno Solin, Alexander Grigorievskiy
We provide a comprehensive overview and tooling for GP modeling with non-Gaussian likelihoods using state space methods. The state space formulation allows for solving one-dimensio…
Scalable Log Determinants for Gaussian Process Kernel Learning
Kun Dong, David Eriksson, Hannes Nickisch +2
For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compu…
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces
William Herlands, Andrew Wilson, Hannes Nickisch +4
We present a scalable Gaussian process model for identifying and characterizing smooth multidimensional changepoints, and automatically learning changes in expressive covariance st…
Additive Gaussian Processes
David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only…
Multiple Kernel Learning: A Unifying Probabilistic Viewpoint
Hannes Nickisch, Matthias Seeger
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations.…