142 citations · 153 across the 10 of their papers we have counts for
10 papers
Optimal worst-risk minimization in structural equation models with random coefficients
Philip Kennerberg, Ernst Wit
The insight that causal parameters are particularly suitable for out-of-sample prediction has sparked a lot development of causal-like predictors. However, the connection with stri…
Fast inference of latent space dynamics in huge relational event networks
Igor Artico, Ernst Wit
Relational events are a type of social interactions, that sometimes are referred to as dynamic networks. Its dynamics typically depends on emerging patterns, so-called endogenous v…
Causal Regularization: On the trade-off between in-sample risk and out-of-sample risk guarantees
Lucas Kania, Ernst Wit
Invariant prediction uses the prediction stability of causal relationships across different environments to identify causal variables. Conversely, using causal variables gives pred…
Generative Models for Periodicity Detection in Noisy Signals
Ezekiel Barnett, Olga Kaiser, Jonathan Masci +2
We introduce a new periodicity detection algorithm for binary time series of event onsets, the Gaussian Mixture Periodicity Detection Algorithm (GMPDA). The algorithm approaches th…
Generalized information criterion for model selection in penalized graphical models
Antonino Abbruzzo, Ivan Vujačić, Ernst Wit +1
This paper introduces an estimator of the relative directed distance between an estimated model and the true model, based on the Kulback-Leibler divergence and is motivated by the…
Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations
Javier González, Ivan Vujačić, Ernst Wit
Non-linear systems of differential equations have attracted the interest in fields like system biology, ecology or biochemistry, due to their flexibility and their ability to descr…
A computationally fast alternative to cross-validation in penalized Gaussian graphical models
Ivan Vujacic, Antonino Abbruzzo, Ernst Wit
We study the problem of selection of regularization parameter in penalized Gaussian graphical models. When the goal is to obtain the model with good predicting power, cross validat…
Bayesian Structure Learning in Sparse Gaussian Graphical Models
A. Mohammadi, E. C. Wit
Decoding complex relationships among large numbers of variables with relatively few observations is one of the crucial issues in science. One approach to this problem is Gaussian g…
Factorial graphical lasso for dynamic networks
E. C. Wit, A. Abbruzzo
Dynamic networks models describe a growing number of important scientific processes, from cell biology and epidemiology to sociology and finance. There are many aspects of dynamica…
Modelling slowly changing dynamic gene-regulatory networks
Antonino Abbruzzo, Ernst Wit
Dynamic gene-regulatory networks are complex since the number of potential components involved in the system is very large. Estimating dynamic networks is an important task because…