882 citations · 2.6k across the 89 of their papers we have counts for
18 papers · 1 filter
Optimistic robust linear quadratic dual control
Jack Umenberger, Thomas B. Schon
Recent work by Mania et al. has proved that certainty equivalent control achieves nearly optimal regret for linear systems with quadratic costs. However, when parameter uncertainty…
Particle filter with rejection control and unbiased estimator of the marginal likelihood
Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön +1
We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties o…
A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors
Manon Kok, Thomas B. Schön
We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration d…
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…
Stochastic quasi-Newton with line-search regularization
Adrian Wills, Thomas Schön
In this paper we present a novel quasi-Newton algorithm for use in stochastic optimisation. Quasi-Newton methods have had an enormous impact on deterministic optimisation problems…
Energy-Based Models for Deep Probabilistic Regression
Fredrik K. Gustafsson, Martin Danelljan, Goutam Bhat +1
While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one parti…
Deep Convolutional Networks in System Identification
Carl Andersson, Antônio H. Ribeiro, Koen Tiels +2
Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system…
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist +1
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle fi…
Robust exploration in linear quadratic reinforcement learning
Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön +1
This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex opti…
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictio…
Neutron Transmission Strain Tomography for Non-Constant Stress-Free Lattice Spacing
J. N. Hendriks, C. Jidling, T. B. Schön +3
Recently, several algorithms for strain tomography from energy-resolved neutron transmission measurements have been proposed. These methods assume that the stress-free lattice spac…
On the smoothness of nonlinear system identification
Antônio H. Ribeiro, Koen Tiels, Jack Umenberger +2
We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems. We show that for regions of…
Automatic diagnosis of the 12-lead ECG using a deep neural network
Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão +9
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacke…
Nonlinear input design as optimal control of a Hamiltonian system
Jack Umenberger, Thomas B. Schön
We propose an input design method for a general class of parametric probabilistic models, including nonlinear dynamical systems with process noise. The goal of the procedure is to…
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…
Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson +3
Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision m…
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…
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding
Muhammad Osama, Dave Zachariah, Thomas B. Schön
We address the problem of inferring the causal effect of an exposure on an outcome across space, using observational data. The data is possibly subject to unmeasured confounding va…