62 citations · 125 across the 26 of their papers we have counts for
5 papers · 1 filter
Assaying Large-scale Testing Models to Interpret COVID-19 Case Numbers
Michel Besserve, Simon Buchholz, Bernhard Schölkopf
Large-scale testing is considered key to assess the state of the current COVID-19 pandemic. Yet, the link between the reported case numbers and the true state of the pandemic remai…
Causal learning with sufficient statistics: an information bottleneck approach
Daniel Chicharro, Michel Besserve, Stefano Panzeri
The inference of causal relationships using observational data from partially observed multivariate systems with hidden variables is a fundamental question in many scientific domai…
Causal Feature Selection via Orthogonal Search
Ashkan Soleymani, Anant Raj, Stefan Bauer +2
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. However, e…
Structure by Architecture: Structured Representations without Regularization
Felix Leeb, Guilia Lanzillotta, Yashas Annadani +3
We study the problem of self-supervised structured representation learning using autoencoders for downstream tasks such as generative modeling. Unlike most methods which rely on ma…
From univariate to multivariate coupling between continuous signals and point processes: a mathematical framework
Shervin Safavi, Nikos K. Logothetis, Michel Besserve
Time series datasets often contain heterogeneous signals, composed of both continuously changing quantities and discretely occurring events. The coupling between these measurements…