Glymour, Clark

25 publications

JMLR 2024 Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang

ICML 2022 Action-Sufficient State Representation Learning for Control with Structural Constraints Biwei Huang, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

ICLRW 2022 Action-Sufficient State Representation Learning for Control with Structural Constraints Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

NeurIPS 2022 Latent Hierarchical Causal Structure Discovery with Rank Constraints Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang

JMLR 2020 Causal Discovery from Heterogeneous/Nonstationary Data Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf

AAAI 2020 Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour

ICMLW 2020 Causal Discovery in the Presence of Missing Values for Neuropathic Pain Diagnosis Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Clark Glymour, Hedvig Kjellström, Cheng Zhang

NeurIPS 2020 Domain Adaptation as a Problem of Inference on Graphical Models Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour

NeurIPS 2020 Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang

ICML 2019 Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour

NeurIPS 2019 Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P Xing, Clark Glymour

NeurIPS 2019 Triad Constraints for Learning Causal Structure of Latent Variables Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang

UAI 2018 Causal Discovery with Linear Non-Gaussian Models Under Measurement Error: Structural Identifiability Results Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour

IJCAI 2017 Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf

UAI 2017 Causal Discovery from Temporally Aggregated Time Series Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao

ICML 2016 Domain Adaptation with Conditional Transferable Components Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf

UAI 2016 On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour

NeurIPS 2008 Integrating Locally Learned Causal Structures with Overlapping Variables David Danks, Clark Glymour, Robert E. Tillman

JMLR 2008 Search for Additive Nonlinear Time Series Causal Models Tianjiao Chu, Clark Glymour

JMLR 2006 Learning the Structure of Linear Latent Variable Models Ricardo Silva, Richard Scheine, Clark Glymour, Peter Spirtes

UAI 2005 On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables Frederick Eberhardt, Clark Glymour, Richard Scheines

UAI 2003 Learning Measurement Models for Unobserved Variables Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes

UAI 2001 Linearity Properties of Bayes Nets with Binary Variables David Danks, Clark Glymour

UAI 1998 Psychological and Normative Theories of Causal Power and the Probabilities of Causes Clark Glymour

AISTATS 1997 A Note on Cyclic Graphs and Dynamical Feedback Systems Thomas S. Richardson, Peter Spirtes, Clark Glymour