Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；LIDS, Massachusetts Institute of Technology（麻省理工学院信息与决策系统实验室）；Broad Institute of MIT and Harvard（布罗德研究所）
Nils Sturma, Chandler Squires, Mathias Drton, Caroline Uhler
The goal of causal representation learning is to find a representation of data that consists of causally related latent variables. We consider a setup where one has access to data from multiple domains that potentially share a causal representation. Crucially, observations in different domains are assumed to be unpaired, that is, we only observe the marginal distribution in each domain but not their joint distribution. In this paper, we give sufficient conditions for identifiability of the joint distribution and the shared causal graph in a linear setup. Identifiability holds if we can uniquely recover the joint distribution and the shared causal representation from the marginal distributions in each domain. We transform our identifiability results into a practical method to recover the shared latent causal graph.
Mix-Domain Contrastive Learning for Unpaired H&E-to-IHC Stain Translation
Toronto Metropolitan University（多伦多都会大学）；Zhengzhou University（郑州大学）；The First Affiliated Hospital of Zhengzhou University（郑州大学第一附属医院）；Zhongyuan University of Technology（中原工学院）
A Sparsity Principle for Partially Observable Causal Representation Learning
University of Amsterdam（阿姆斯特丹大学）；Institute of Science and Technology Austria（奥地利科技学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Samsung - SAIT AI Lab, Montreal（三星SAIT AI实验室（蒙特利尔））；Mila, Université de Montréal（蒙特利尔大学Mila研究所）；ServiceNow Research（ServiceNow研究院）；Seminar for Statistics, ETH Zürich（苏黎世联邦理工学院统计研究所）
Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?
Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系）；Department of Statistics, UC Berkeley（伯克利大学统计系）；German Center for Neurodegenerative Diseases (DZNE) & University of Bonn（德国神经退行性疾病研究中心（DZNE）及波恩大学）；MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计学单位）
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学布罗德研究所）；Laboratory for Information and Decision Systems, MIT（麻省理工学院信息与决策系统实验室）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；IBM Research（IBM研究院）；Google Research（谷歌研究院）
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
National Center for Tumor Diseases（国家肿瘤疾病中心）；University College London（伦敦大学学院）；Mannheim University of Applied Sciences（曼海姆应用科学大学）；German Cancer Research Center（德国癌症研究中心）
Do causal predictors generalize better to new domains?
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Tübingen AI Center（蒂宾根人工智能中心）；Max Planck ETH Center for Learning Systems（马克斯·普朗克ETH学习系统中心）
Causal Mechanism Estimation in Multi-Sensor Systems Across Multiple Domains
Bosch Center for Artificial Intelligence（博世人工智能中心）；Institute of Industrial Manufacturing and Management IFF, University of Stuttgart（工业制造与管理研究所）；Fraunhofer Institute for Manufacturing Engineering and Automation IPA（弗劳恩霍夫制造工程与自动化研究所）
