Targeted Reduction of Causal Models

no code implementations • 30 Nov 2023 • Armin Kekić, Bernhard Schölkopf, Michel Besserve

Why does a phenomenon occur?

Causal Component Analysis

1 code implementation • NeurIPS 2023 • Liang Wendong, Armin Kekić, Julius von Kügelgen, Simon Buchholz, Michel Besserve, Luigi Gresele, Bernhard Schölkopf

As a corollary, this interventional perspective also leads to new identifiability results for nonlinear ICA -- a special case of CauCA with an empty graph -- requiring strictly fewer datasets than previous results.

Representation Learning

Deep Learning based Forecasting: a case study from the online fashion industry

no code implementations • 23 May 2023 • Manuel Kunz, Stefan Birr, Mones Raslan, Lei Ma, Zhen Li, Adele Gouttes, Mateusz Koren, Tofigh Naghibi, Johannes Stephan, Mariia Bulycheva, Matthias Grzeschik, Armin Kekić, Michael Narodovitch, Kashif Rasul, Julian Sieber, Tim Januschowski

These include the volume of data, the irregularity, the high amount of turn-over in the catalog and the fixed inventory assumption.

Evaluating vaccine allocation strategies using simulation-assisted causal modelling

1 code implementation • 14 Dec 2022 • Armin Kekić, Jonas Dehning, Luigi Gresele, Julius von Kügelgen, Viola Priesemann, Bernhard Schölkopf

Early on during a pandemic, vaccine availability is limited, requiring prioritisation of different population groups.

counterfactual

DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability

1 code implementation • 1 Oct 2022 • Cian Eastwood, Andrei Liviu Nicolicioiu, Julius von Kügelgen, Armin Kekić, Frederik Träuble, Andrea Dittadi, Bernhard Schölkopf

In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation.

Disentanglement Informativeness