882 citations · 2.6k across the 89 of their papers we have counts for
30 papers · 1 filter
A Rigorous, Tractable Measure of Model Complexity
Oskar Allerbo, Thomas B. Schön
An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, most existing complexity measures either…
Regularization properties of adversarially-trained linear regression
Antônio H. Ribeiro, Dave Zachariah, Francis Bach +1
State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to de…
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals
Zheng Zhao, Simo Särkkä, Jens Sjölund +1
We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessib…
Incorporating Sum Constraints into Multitask Gaussian Processes
Philipp Pilar, Carl Jidling, Thomas B. Schön +1
Machine learning models can be improved by adapting them to respect existing background knowledge. In this paper we consider multitask Gaussian processes, with background knowledge…
Efficient Learning of the Parameters of Non-Linear Models using Differentiable Resampling in Particle Filters
Conor Rosato, Vincent Beraud, Paul Horridge +2
It has been widely documented that the sampling and resampling steps in particle filters cannot be differentiated. The {\itshape reparameterisation trick} was introduced to allow t…
Variational State and Parameter Estimation
Jarrad Courts, Johannes Hendriks, Adrian Wills +2
This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tract…
Variational System Identification for Nonlinear State-Space Models
Jarrad Courts, Adrian Wills, Thomas Schön +1
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational infe…
Gaussian Variational State Estimation for Nonlinear State-Space Models
Jarrad Courts, Adrian Wills, Thomas B. Schön
In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the m…
Linearly Constrained Neural Networks
Johannes Hendriks, Carl Jidling, Adrian Wills +1
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints. To achiev…
Deep kernel learning for integral measurements
Carl Jidling, Johannes Hendriks, Thomas B. Schön +1
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions. We present a method that makes this approach feas…
Elements of Sequential Monte Carlo
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and…
Constructing the Matrix Multilayer Perceptron and its Application to the VAE
Jalil Taghia, Maria Bånkestad, Fredrik Lindsten +1
Like most learning algorithms, the multilayer perceptrons (MLP) is designed to learn a vector of parameters from data. However, in certain scenarios we are interested in learning s…
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations
J. N. Hendriks, C. Jidling, A. Wills +1
This paper deals with the evaluation of double line integrals of the squared exponential covariance function. We propose a new approach in which the double integral is reduced to a…
Automated learning with a probabilistic programming language: Birch
Lawrence M. Murray, Thomas B. Schön
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Tur…
Learning convex bounds for linear quadratic control policy synthesis
Jack Umenberger, Thomas B. Schön
Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a number of fields, from artificial intelligence and robotics,…
Stochastic quasi-Newton with adaptive step lengths for large-scale problems
Adrian Wills, Thomas Schön
We provide a numerically robust and fast method capable of exploiting the local geometry when solving large-scale stochastic optimisation problems. Our key innovation is an auxilia…
Conditionally Independent Multiresolution Gaussian Processes
Jalil Taghia, Thomas B. Schön
The multiresolution Gaussian process (GP) has gained increasing attention as a viable approach towards improving the quality of approximations in GPs that scale well to large-scale…
Learning Localized Spatio-Temporal Models From Streaming Data
Muhammad Osama, Dave Zachariah, Thomas B. Schön
We address the problem of predicting spatio-temporal processes with temporal patterns that vary across spatial regions, when data is obtained as a stream. That is, when the trainin…
How consistent is my model with the data? Information-Theoretic Model Check
Andreas Svensson, Dave Zachariah, Thomas B. Schön
The choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-bo…
Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs
Lawrence M. Murray, Daniel Lundén, Jan Kudlicka +2
We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce vari…
On the construction of probabilistic Newton-type algorithms
Adrian G. Wills, Thomas B. Schön
It has recently been shown that many of the existing quasi-Newton algorithms can be formulated as learning algorithms, capable of learning local models of the cost functions. Impor…
Linearly constrained Gaussian processes
Carl Jidling, Niklas Wahlström, Adrian Wills +1
We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints. By modelling the target function as a transformation…
Prediction performance after learning in Gaussian process regression
Johan Wågberg, Dave Zachariah, Thomas B. Schön +1
This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical…
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden +1
Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. It approximates the obj…