Clark Glymour
According to our database1,
Clark Glymour
authored at least 71 papers
between 1972 and 2023.
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Bibliography
2023
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables.
CoRR, 2023
2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Action-Sufficient State Representation Learning for Control with Structural Constraints.
Proceedings of the International Conference on Machine Learning, 2022
2021
CoRR, 2021
2020
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs.
CoRR, 2020
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
2019
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data.
CoRR, 2019
Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis.
Bioinform., 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.
Proceedings of the 36th International Conference on Machine Learning, 2019
2018
Comparison of strategies for scalable causal discovery of latent variable models from mixed data.
Int. J. Data Sci. Anal., 2018
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018
2017
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., 2017
CoRR, 2017
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
Proceedings of the 2017 IEEE International Conference on Data Mining, 2017
2016
Clark Glymour's responses to the contributions to the Synthese special issue "Causation, probability, and truth: the philosophy of Clark Glymour".
Synth., 2016
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016
Proceedings of the 33nd International Conference on Machine Learning, 2016
2015
J. Am. Medical Informatics Assoc., 2015
2014
Non-Gaussian methods and high-pass filters in the estimation of effective connections.
NeuroImage, 2014
2013
Counterfactuals, graphical causal models and potential outcomes: Response to Lindquist and Sobel.
NeuroImage, 2013
Brain Connect., 2013
2012
2011
Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study.
NeuroImage, 2011
2010
Int. J. Comput. Heal., 2010
Using Causal Modeling for Determining Connectivity among Brain Regions.
Proceedings of the 2010 International Conference on Artificial Intelligence, 2010
2008
Proceedings of the Advances in Neural Information Processing Systems 21, 2008
2007
Proceedings of the Induction, Algorithmic Learning Theory, and Philosophy, 2007
2006
2005
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables.
Proceedings of the UAI '05, 2005
2003
A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays.
Bioinform., 2003
Proceedings of the UAI '03, 2003
2002
Intell. Data Anal., 2002
Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition.
Data Min. Knowl. Discov., 2002
2001
Proceedings of the UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 2001
2000
Synth., 2000
Causation, Prediction, and Search, Second Edition.
Adaptive computation and machine learning, MIT Press, ISBN: 978-0-262-19440-2, 2000
1999
1998
Psychological and Normative Theories of Causal Power and the Probabilities of Causes.
Proceedings of the UAI '98: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, 1998
1997
Artif. Intell. Medicine, 1997
1996
1995
Available Technology for Discovering Causal Models, Building Bayes Nets, and Selecting Predictors: The TETRAD II Program.
Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95), 1995
1994
Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges.
Proceedings of the Knowledge Discovery in Databases: Papers from the 1994 AAAI Workshop, 1994
1992
1991
1990
1985
1984
Default Reasoning and the Logic of Theory Perturbation.
Proceedings of the Non-Monotonic Reasoning Workshop, 1984
1972