1.6k citations · 12.4k across the 394 of their papers we have counts for
9 papers · 2 filters
Spectral Representation Learning for Conditional Moment Models
Ziyu Wang, Yucen Luo, Yueru Li +2
Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of cond…
On the Identifiability and Estimation of Causal Location-Scale Noise Models
Alexander Immer, Christoph Schultheiss, Julia E. Vogt +3
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect can be written as a function of the cause and a noise source independe…
Function Classes for Identifiable Nonlinear Independent Component Analysis
Simon Buchholz, Michel Besserve, Bernhard Schölkopf
Unsupervised learning of latent variable models (LVMs) is widely used to represent data in machine learning. When such models reflect the ground truth factors and the mechanisms ma…
Probing the Robustness of Independent Mechanism Analysis for Representation Learning
Joanna Sliwa, Shubhangi Ghosh, Vincent Stimper +2
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. A recently prop…
Probable Domain Generalization via Quantile Risk Minimization
Cian Eastwood, Alexander Robey, Shashank Singh +4
Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains. To ach…
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Patrik Reizinger, Luigi Gresele, Jack Brady +6
Variational autoencoders (VAEs) are a popular framework for modeling complex data distributions; they can be efficiently trained via variational inference by maximizing the evidenc…
Phenomenology of Double Descent in Finite-Width Neural Networks
Sidak Pal Singh, Aurelien Lucchi, Thomas Hofmann +1
`Double descent' delineates the generalization behaviour of models depending on the regime they belong to: under- or over-parameterized. The current theoretical understanding behin…
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
Siyuan Guo, Viktor Tóth, Bernhard Schölkopf +1
Constraint-based causal discovery methods leverage conditional independence tests to infer causal relationships in a wide variety of applications. Just as the majority of machine l…
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"
Shubhangi Ghosh, Luigi Gresele, Julius von Kügelgen +2
Model identifiability is a desirable property in the context of unsupervised representation learning. In absence thereof, different models may be observationally indistinguishable…