Clark Glymour - RankMe

81 papers A* 13A 8Misc 1Journal 53Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. J. Mach. Learn. Res. Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang
2023	J	`jnl`	Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. CoRR Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang
2022	A*	`conf`	Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
2022	A*	`conf`	Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang
2022	J	`jnl`	Latent Hierarchical Causal Structure Discovery with Rank Constraints. CoRR Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang
2021	J	`jnl`	Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
2021	J	`jnl`	FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. CoRR Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph D. Ramsey, Zhifeng Hao, Clark Glymour
2020	J	`jnl`	Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
2020	A*	`conf`	Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour
2020	J	`jnl`	Domain Adaptation As a Problem of Inference on Graphical Models. CoRR Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Clark Glymour
2020	A*	`conf`	Domain Adaptation as a Problem of Inference on Graphical Models. NeurIPS Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour
2020	A*	`conf`	Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang
2020	J	`jnl`	Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. CoRR Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang
2019	A*	`conf`	Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour
2019	J	`jnl`	Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. CoRR Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour
2019	J	`jnl`	Causal Discovery from Heterogeneous/Nonstationary Data. CoRR Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
2019	J	`jnl`	Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. CoRR Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph D. Ramsey, Madelyn Glymour, Clark Glymour
2019	J	`jnl`	Identification of Effective Connectivity Subregions. CoRR Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Clark Glymour
2019	J	`jnl`	Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. Bioinform. Andrew J. Sedgewick, Kristina Buschur, Ivy Shi, Joseph D. Ramsey, Vineet K. Raghu, Dimitris V. Manatakis, Yingze Zhang, Jessica Bon, Divay Chandra, Chad Karoleski, Frank C. Sciurba, Peter Spirtes, Clark Glymour, Panayiotis V. Benos
2019	A*	`conf`	Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. NeurIPS Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour
2019	A*	`conf`	Triad Constraints for Learning Causal Structure of Latent Variables. NeurIPS Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang
2018	A	`conf`	Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour
2018	J	`jnl`	Causal Generative Domain Adaptation Networks. CoRR Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, Dacheng Tao, Kayhan Batmanghelich
2018	J	`jnl`	Comparison of strategies for scalable causal discovery of latent variable models from mixed data. Int. J. Data Sci. Anal. Vineet K. Raghu, Joseph D. Ramsey, Alison Morris, Dimitrios V. Manatakis, Peter Spirtes, Panos K. Chrysanthis, Clark Glymour, Panayiotis V. Benos
2018	A*	`conf`	Generalized Score Functions for Causal Discovery. KDD Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour
2017	J	`jnl`	A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images. Int. J. Data Sci. Anal. Joseph D. Ramsey, Madelyn Glymour, Ruben Sanchez-Romero, Clark Glymour
2017	A*	`conf`	Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM Biwei Huang, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
2017	A*	`conf`	Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf
2017	A	`conf`	Causal Discovery from Temporally Aggregated Time Series. UAI Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao
2017	J	`jnl`	Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. CoRR Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour
2017	J	`jnl`	Mixed Graphical Models for Causal Analysis of Multi-modal Variables. CoRR Andrew J. Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos
2016	J	`jnl`	Clark Glymour's responses to the contributions to the Synthese special issue "Causation, probability, and truth: the philosophy of Clark Glymour". Synth. Clark Glymour
2016	A*	`conf`	Domain Adaptation with Conditional Transferable Components. ICML Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf
2016	A	`conf`	On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour
2015	J	`jnl`	Cognitive Orthoses: Toward Human-Centered AI. AI Mag. Kenneth M. Ford, Patrick J. Hayes, Clark Glymour, James F. Allen
2015	J	`jnl`	The center for causal discovery of biomedical knowledge from big data. J. Am. Medical Informatics Assoc. Gregory F. Cooper, Ivet Bahar, Michael J. Becich, Panayiotis V. Benos, Jeremy M. Berg, Jeremy U. Espino, Clark Glymour, Rebecca Crowley Jacobson, Michelle Kienholz, Adrian V. Lee, Xinghua Lu, Richard Scheines
2014	J	`jnl`	Non-Gaussian methods and high-pass filters in the estimation of effective connections. NeuroImage Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour
2013	J	`jnl`	Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism. Brain Connect. Catherine Hanson, Stephen José Hanson, Joseph D. Ramsey, Clark Glymour
2013	J	`jnl`	Counterfactuals, graphical causal models and potential outcomes: Response to Lindquist and Sobel. NeuroImage Clark Glymour
2013	J	`jnl`	Linearity Properties of Bayes Nets with Binary Variables CoRR David Danks, Clark Glymour
2013	J	`jnl`	Psychological and Normative Theories of Causal Power and the Probabilities of Causes CoRR Clark Glymour
2012	J	`jnl`	Learning Measurement Models for Unobserved Variables CoRR Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes
2012	J	`jnl`	On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables CoRR Frederick Eberhardt, Clark Glymour, Richard Scheines
2012	J	`jnl`	On the Possibility of Inference to the Best Explanation. J. Philos. Log. Clark Glymour
2011	—	`ch.`	Hans Reichenbach's Probability Logic. Inductive Logic Frederick Eberhardt, Clark Glymour
2011	J	`jnl`	Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage Joseph D. Ramsey, Stephen José Hanson, Clark Glymour
2011	J	`jnl`	On meta-analyses of imaging data and the mixture of records. NeuroImage Joseph D. Ramsey, Peter Spirtes, Clark Glymour
2010	J	`jnl`	Actual causation: a stone soup essay. Synth. Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang
2010	J	`jnl`	Discovering effective connectivity among brain regions from functional MRI data. Int. J. Comput. Heal. Carlos Perez, Eman El-Sheikh, Clark Glymour
2010	J	`jnl`	Six problems for causal inference from fMRI. NeuroImage Joseph D. Ramsey, Stephen José Hanson, Catherine Hanson, Yaroslav O. Halchenko, Russell A. Poldrack, Clark Glymour
2010	Misc	`conf`	Using Causal Modeling for Determining Connectivity among Brain Regions. IC-AI Eman El-Sheikh, Carlos Perez, Clark Glymour
2008	—	`conf`	Integrating Locally Learned Causal Structures with Overlapping Variables. NIPS Robert E. Tillman, David Danks, Clark Glymour
2008	J	`jnl`	Search for Additive Nonlinear Time Series Causal Models. J. Mach. Learn. Res. Tianjiao Chu, Clark Glymour
2007	—	`ch.`	Trade-Offs. Induction, Algorithmic Learning Theory, and Philosophy Clark Glymour
2006	J	`jnl`	Learning the Structure of Linear Latent Variable Models. J. Mach. Learn. Res. Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes
2005	A	`conf`	On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. UAI Frederick Eberhardt, Clark Glymour, Richard Scheines
2003	J	`jnl`	A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays. Bioinform. Tianjiao Chu, Clark Glymour, Richard Scheines, Peter Spirtes
2003	A	`conf`	Learning Measurement Models for Unobserved Variables. UAI Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes
2002	J	`jnl`	Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition. Data Min. Knowl. Discov. Joseph D. Ramsey, Paul Gazis, Ted Roush, Peter Spirtes, Clark Glymour
2002	J	`jnl`	Classification and filtering of spectra: A case study in mineralogy. Intell. Data Anal. Jonathan Moody, Ricardo Bezerra de Andrade e Silva, Joseph Vanderwaart, Joseph D. Ramsey, Clark Glymour
2001	A	`conf`	Linearity Properties of Bayes Nets with Binary Variables. UAI David Danks, Clark Glymour
2000	J	`jnl`	Android Epistemology For Babies: Relections On Words, Thoughts And Theories. Synth. Clark Glymour
2000	—	`book`	Causation, Prediction, and Search, Second Edition. Peter Spirtes, Clark Glymour, Richard Scheines
1999	J	`jnl`	Rabbit Hunting. Synth. Clark Glymour
1998	J	`jnl`	Buy and Use Thinking Things Through. Minds Mach. Clark Glymour
1998	J	`jnl`	Learning Causes: Psychological Explanations of Causal Explanation. Minds Mach. Clark Glymour
1998	A	`conf`	Psychological and Normative Theories of Causal Power and the Probabilities of Causes. UAI Clark Glymour
1998	J	`jnl`	Ramón Lull and the Infidels. AI Mag. Clark Glymour, Kenneth M. Ford, Patrick J. Hayes
1997	A	`conf`	A Note on Cyclic Graphs and Dynamical Feedback Systems. AISTATS Thomas S. Richardson, Peter Spirtes, Clark Glymour
1997	J	`jnl`	An evaluation of machine-learning methods for predicting pneumonia mortality. Artif. Intell. Medicine Gregory F. Cooper, Constantin F. Aliferis, Richard Ambrosino, John M. Aronis, Bruce G. Buchanan, Rich Caruana, Michael J. Fine, Clark Glymour, Geoffrey J. Gordon, Barbara H. Hanusa, Janine E. Janosky, Christopher Meek, Tom M. Mitchell, Thomas S. Richardson, Peter Spirtes
1997	J	`jnl`	On the Other Hand - Cognitive Prostheses. AI Mag. Kenneth M. Ford, Clark Glymour, Patrick J. Hayes
1997	J	`jnl`	Statistical Themes and Lessons for Data Mining. Data Min. Knowl. Discov. Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth
1996	J	`jnl`	Statistical Inference and Data Mining. Commun. ACM Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth
1995	A*	`conf`	Available Technology for Discovering Causal Models, Building Bayes Nets, and Selecting Predictors: The TETRAD II Program. KDD Clark Glymour
1994	—	`conf`	Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges. KDD Workshop Marek J. Druzdze, Clark Glymour
1992	J	`jnl`	Inductive inference from theory laden data. J. Philos. Log. Kevin T. Kelly, Clark Glymour
1991	J	`jnl`	The hierarchies of knowledge and the mathematics of discovery. Minds Mach. Clark Glymour
1990	J	`jnl`	Theory discovery from data with mixed quantifiers. J. Philos. Log. Kevin T. Kelly, Clark Glymour
1985	J	`jnl`	Independence Assumptions and Bayesian Updating. Artif. Intell. Clark Glymour
1984	—	`conf`	Default Reasoning and the Logic of Theory Perturbation. NMR Clark Glymour, Richmond H. Thomason
1972	J	`jnl`	If quanta had logic. J. Philos. Log. Michael Friedman, Clark Glymour