no code implementations • 20 Dec 2023 • Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf
As typical in ICA, previous work focused on the case with an equal number of latent components and observed mixtures.
1 code implementation • NeurIPS 2023 • Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez Adauto, Max Kleiman-Weiner, Mrinmaya Sachan, Bernhard Schölkopf
Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules.
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.
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.
no code implementations • 13 Jul 2022 • Joanna Sliwa, Shubhangi Ghosh, Vincent Stimper, Luigi Gresele, Bernhard Schölkopf
One aim of representation learning is to recover the original latent code that generated the data, a task which requires additional information or inductive biases.
1 code implementation • 6 Jun 2022 • Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve
Leveraging self-consistency, we show that the ELBO converges to a regularized log-likelihood.
no code implementations • 14 Feb 2022 • Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen, Michel Besserve, Bernhard Schölkopf
Model identifiability is a desirable property in the context of unsupervised representation learning.
1 code implementation • 2 Feb 2022 • Luigi Gresele, Julius von Kügelgen, Jonas M. Kübler, Elke Kirschbaum, Bernhard Schölkopf, Dominik Janzing
We introduce an approach to counterfactual inference based on merging information from multiple datasets.
1 code implementation • NeurIPS 2021 • Luigi Gresele, Julius von Kügelgen, Vincent Stimper, Bernhard Schölkopf, Michel Besserve
Specifically, our approach is motivated by thinking of each source as independently influencing the mixing process.
1 code implementation • NeurIPS 2021 • Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello
A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant.
Ranked #1 on Image Classification on Causal3DIdent
3 code implementations • ICLR 2021 • Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, Bernhard Schölkopf
In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning.
1 code implementation • NeurIPS 2020 • Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen
Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.
1 code implementation • NeurIPS 2020 • Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.
1 code implementation • 14 May 2020 • Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf
We point out limitations and extensions for future work, and, finally, discuss the role of causal reasoning in the broader context of using AI to combat the Covid-19 pandemic.
Applications Methodology
no code implementations • 29 May 2019 • Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet
The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences.
no code implementations • 16 May 2019 • Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf
In contrast to known identifiability results for nonlinear ICA, we prove that independent latent sources with arbitrary mixing can be recovered as long as multiple, sufficiently different noisy views are available.
no code implementations • 6 Mar 2019 • Anant Raj, Luigi Gresele, Michel Besserve, Bernhard Schölkopf, Stefan Bauer
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.