D'ya like dags? a survey on structure learning and causal discovery
Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …
causal relationships from data, we need structure discovery methods. We provide a review …
Causal discovery from temporal data: An overview and new perspectives
Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …
been a typical data structure that can be widely generated by many domains, such as …
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …
of clinical experts. However, in settings differing from those of the training dataset, the …
Towards out-of-distribution generalization: A survey
Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …
test data follow the same statistical pattern, which is mathematically referred to as …
Disentangled representation learning
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …
and disentangling the underlying factors hidden in the observable data in representation …
Weakly supervised causal representation learning
Learning high-level causal representations together with a causal model from unstructured
low-level data such as pixels is impossible from observational data alone. We prove under …
low-level data such as pixels is impossible from observational data alone. We prove under …
Good is bad: Causality inspired cloth-debiasing for cloth-changing person re-identification
Entangled representation of clothing and identity (ID)-intrinsic clues are potentially
concomitant in conventional person Re-IDentification (ReID). Nevertheless, eliminating the …
concomitant in conventional person Re-IDentification (ReID). Nevertheless, eliminating the …
Nonparametric identifiability of causal representations from unknown interventions
J von Kügelgen, M Besserve… - Advances in …, 2024 - proceedings.neurips.cc
We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …
Information theoretic learning-enhanced dual-generative adversarial networks with causal representation for robust OOD generalization
Recently, machine/deep learning techniques are achieving remarkable success in a variety
of intelligent control and management systems, promising to change the future of artificial …
of intelligent control and management systems, promising to change the future of artificial …
Linear causal disentanglement via interventions
Causal disentanglement seeks a representation of data involving latent variables that are
related via a causal model. A representation is identifiable if both the latent model and the …
related via a causal model. A representation is identifiable if both the latent model and the …