activity
20152021
most citedTelling cause from effect in deterministic linear dynamical systems

25 citations · 27 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME2021

Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations

Michel Besserve, Naji Shajarisales, Dominik Janzing +1

Distinguishing between cause and effect using time series observational data is a major challenge in many scientific fields. A new perspective has been provided based on the princi…

cs.LG20201 cited

Learning from Positive and Unlabeled Data by Identifying the Annotation Process

Naji Shajarisales, Peter Spirtes, Kun Zhang

In binary classification, Learning from Positive and Unlabeled data (LePU) is semi-supervised learning but with labeled elements from only one class. Most of the research on LePU r…

math.PR20171 cited

A central limit like theorem for Fourier sums

Dominik Janzing, Naji Shajarisales, Michel Besserve

We consider the probability distributions of values in the complex plane attained by Fourier sums of the form \sum_{j=1}^n a_j exp(-2πi j nu) /sqrt{n} when the frequency nu is draw…

stat.ML2017

Group invariance principles for causal generative models

Michel Besserve, Naji Shajarisales, Bernhard Schölkopf +1

The postulate of independence of cause and mechanism (ICM) has recently led to several new causal discovery algorithms. The interpretation of independence and the way it is utilize…

cs.AI201525 cited

Telling cause from effect in deterministic linear dynamical systems

Naji Shajarisales, Dominik Janzing, Bernhard Shoelkopf +1

Inferring a cause from its effect using observed time series data is a major challenge in natural and social sciences. Assuming the effect is generated by the cause trough a linear…