D'ya like dags? a survey on structure learning and causal discovery

MJ Vowels, NC Camgoz, R Bowden - ACM Computing Surveys, 2022 - dl.acm.org
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 discovery from temporal data: An overview and new perspectives

C Gong, C Zhang, D Yao, J Bi, W Li, YJ Xu - ACM Computing Surveys, 2023 - dl.acm.org
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 …

Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

S Azizi, L Culp, J Freyberg, B Mustafa, S Baur… - Nature Biomedical …, 2023 - nature.com
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 …

Towards out-of-distribution generalization: A survey

J Liu, Z Shen, Y He, X Zhang, R Xu, H Yu… - arXiv preprint arXiv …, 2021 - arxiv.org
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 …

Disentangled representation learning

X Wang, H Chen, Z Wu, W Zhu - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Weakly supervised causal representation learning

J Brehmer, P De Haan, P Lippe… - Advances in Neural …, 2022 - proceedings.neurips.cc
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 …

Good is bad: Causality inspired cloth-debiasing for cloth-changing person re-identification

Z Yang, M Lin, X Zhong, Y Wu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Entangled representation of clothing and identity (ID)-intrinsic clues are potentially
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 …

Information theoretic learning-enhanced dual-generative adversarial networks with causal representation for robust OOD generalization

X Zhou, X Zheng, T Shu, W Liang… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
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 …

Linear causal disentanglement via interventions

C Squires, A Seigal, SS Bhate… - … Conference on Machine …, 2023 - proceedings.mlr.press
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 …