no code implementations • 14 Feb 2024 • Goutham Rajendran, Simon Buchholz, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
In this work, we relate these two approaches and study how to learn human-interpretable concepts from data.
no code implementations • NeurIPS 2023 • Simon Buchholz, Goutham Rajendran, Elan Rosenfeld, Bryon Aragam, Bernhard Schölkopf, Pradeep Ravikumar
We study the problem of learning causal representations from unknown, latent interventions in a general setting, where the latent distribution is Gaussian but the mixing function is completely general.
1 code implementation • NeurIPS 2023 • Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf
Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging.
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.
no code implementations • 20 Jan 2023 • Simon Buchholz, Jonas M. Kübler, Bernhard Schölkopf
Here we introduce further bandit models where we only have limited access to the randomness of the rewards, but we can still query the arms in superposition.
no code implementations • 12 Aug 2022 • Simon Buchholz, Michel Besserve, Bernhard Schölkopf
Several families of spurious solutions fitting perfectly the data, but that do not correspond to the ground truth factors can be constructed in generic settings.
3 code implementations • 17 Jun 2022 • Jonas M. Kübler, Vincent Stimper, Simon Buchholz, Krikamol Muandet, Bernhard Schölkopf
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
1 code implementation • NeurIPS 2021 • Jonas M. Kübler, Simon Buchholz, Bernhard Schölkopf
Quantum computers offer the possibility to efficiently compute inner products of exponentially large density operators that are classically hard to compute.
no code implementations • 3 Dec 2020 • Michel Besserve, Simon Buchholz, Bernhard Schölkopf
Large-scale testing is considered key to assess the state of the current COVID-19 pandemic.
Applications Populations and Evolution