activity
20142019
most citedEvaluating model calibration in classification

91 citations · 116 across the 7 of their papers we have counts for

collaborators

7 papers

math.OC20192 cited

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…

cs.LG201991 cited

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…

stat.ML20194 cited

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…

stat.ME20192 cited

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…

stat.ML20188 cited

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…

stat.ME20169 cited

Coupling of Particle Filters

Pierre E. Jacob, Fredrik Lindsten, Thomas B. Schön

Particle filters provide Monte Carlo approximations of intractable quantities such as point-wise evaluations of the likelihood in state space models. In many scenarios, the interes…