TL;DR
The results suggest that the architectural prior provides a useful organization of contextual information, while exposure to missing-modality cases is still needed for robust fallback when auxiliary inputs are absent.
Conditional independence testing is a ubiquitous problem in scientific discovery. The widely employed model-X assumption shifts the modelling burden from the dependence of the output on the inputs to the dependencies within the inputs. Log-optimal e-variables have been studied in this setting, but it remains unclear ho...
This paper organizes methods by prediction target, prediction granularity, encoder, structural decoder, and training regime to relate what each method predicts to how it uses data and simulator-based supervision, and distinguishes two questions: whether the target is identifiable under the assumed model class and wheth...
We study robust prediction when the training data come from a single observational distribution. Causality-oriented methods for robust prediction usually use heterogeneity across observed environments both to learn an invariant prediction function and to determine the perturbations against which it is robust. We show t...
A model-agnostic framework based on full conditional distributions distinguishes three statistically different failure modes: loss of dependence, record reuse, and mode concentration, and provides a principled basis for validating synthetic health, surveillance, and population data.
Modern deep generative models are primarily studied for their ability to generate realistic samples, yet the generative dynamics they learn can also serve as objects of statistical inference. We develop this idea for two-sample testing, the problem of deciding whether the same distribution generated two finite datasets...
SaCRL is introduced, a framework that jointly identifies the causal structure and learns the corresponding invariant representation without prior structural knowledge, and provides theoretical guarantees for structure identification, including under random-feature approximation, invariance satisfaction, and out-of-dist...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
