research
∙
06/01/2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions
We study causal representation learning, the task of inferring latent ca...
share
research
∙
05/26/2023
Causal Component Analysis
Independent Component Analysis (ICA) aims to recover independent latent ...
research
∙
09/15/2022
Information Theoretic Measures of Causal Influences during Transient Neural Events
Transient phenomena play a key role in coordinating brain activity at mu...
research
∙
08/12/2022
Function Classes for Identifiable Nonlinear Independent Component Analysis
Unsupervised learning of latent variable models (LVMs) is widely used to...
research
∙
07/25/2022
Homomorphism Autoencoder – Learning Group Structured Representations from Observed Transitions
How can we acquire world models that veridically represent the outside w...
research
∙
06/06/2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Variational autoencoders (VAEs) are a popular framework for modeling com...
research
∙
04/29/2022
Bayesian Information Criterion for Event-based Multi-trial Ensemble data
Transient recurring phenomena are ubiquitous in many scientific fields l...
research
∙
02/14/2022
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"
Model identifiability is a desirable property in the context of unsuperv...
research
∙
12/10/2021
Learning soft interventions in complex equilibrium systems
Complex systems often contain feedback loops that can be described as cy...
research
∙
10/29/2021
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations
Distinguishing between cause and effect using time series observational ...
research
∙
06/09/2021
Independent mechanism analysis, a new concept?
Independent component analysis provides a principled framework for unsup...
research
∙
06/08/2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Self-supervised representation learning has shown remarkable success in ...
research
∙
12/03/2020
Assaying Large-scale Testing Models to Interpret Covid-19 Case Numbers. A Cross-country Study
Large-scale testing is considered key to assessing the state of the curr...
research
∙
10/12/2020
Causal learning with sufficient statistics: an information bottleneck approach
The inference of causal relationships using observational data from part...
research
∙
07/06/2020
Causal Feature Selection via Orthogonal Search
The problem of inferring the direct causal parents of a response variabl...
research
∙
05/08/2020
From univariate to multivariate coupling between continuous signals and point processes: a mathematical framework
Time series datasets often contain heterogeneous signals, composed of bo...
research
∙
04/01/2020
A theory of independent mechanisms for extrapolation in generative models
Deep generative models reproduce complex empirical data but cannot extra...
research
∙
03/06/2019
Orthogonal Structure Search for Efficient Causal Discovery from Observational Data
The problem of inferring the direct causal parents of a response variabl...
research
∙
12/08/2018
Counterfactuals uncover the modular structure of deep generative models
Deep generative models such as Generative Adversarial Networks (GANs) an...
research
∙
03/16/2018
Coordination via predictive assistants from a game-theoretic view
We study machine learning-based assistants that support coordination bet...
research
∙
05/05/2017
Group invariance principles for causal generative models
The postulate of independence of cause and mechanism (ICM) has recently ...
research
∙
03/04/2015
Telling cause from effect in deterministic linear dynamical systems
Inferring a cause from its effect using observed time series data is a m...
research
∙
09/25/2012