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Clark Glymour
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2020 – today
- 2024
- [j36]Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang:
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. J. Mach. Learn. Res. 25: 191:1-191:61 (2024) - 2023
- [i17]Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang:
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. CoRR abs/2308.06718 (2023) - 2022
- [c25]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML 2022: 9260-9279 - [c24]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS 2022 - [i16]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. CoRR abs/2210.01798 (2022) - 2021
- [i15]Wei Chen
, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph D. Ramsey, Zhifeng Hao, Clark Glymour:
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. CoRR abs/2103.14238 (2021) - [i14]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR abs/2110.05721 (2021) - 2020
- [j35]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. 21: 89:1-89:53 (2020) - [c23]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI 2020: 10153-10161 - [c22]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour:
Domain Adaptation as a Problem of Inference on Graphical Models. NeurIPS 2020 - [c21]Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS 2020 - [i13]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Clark Glymour:
Domain Adaptation As a Problem of Inference on Graphical Models. CoRR abs/2002.03278 (2020) - [i12]Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. CoRR abs/2010.04917 (2020)
2010 – 2019
- 2019
- [j34]Andrew J. Sedgewick, Kristina Buschur, Ivy Shi, Joseph D. Ramsey, Vineet K. Raghu, Dimitris V. Manatakis, Yingze Zhang
Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. Bioinform. 35(7): 1204-1212 (2019) - [c20]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML 2019: 2901-2910 - [c19]Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang:
Triad Constraints for Learning Causal Structure of Latent Variables. NeurIPS 2019: 12863-12872 - [c18]Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour:
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. NeurIPS 2019: 13510-13521 - [i11]Biwei Huang, Kun Zhang, Ruben Sanchez-Romero
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. CoRR abs/1902.10073 (2019) - [i10]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero
Causal Discovery from Heterogeneous/Nonstationary Data. CoRR abs/1903.01672 (2019) - [i9]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. CoRR abs/1905.10857 (2019) - [i8]Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Clark Glymour:
Identification of Effective Connectivity Subregions. CoRR abs/1908.03264 (2019) - 2018
- [j33]Vineet K. Raghu
Comparison of strategies for scalable causal discovery of latent variable models from mixed data. Int. J. Data Sci. Anal. 6(1): 33-45 (2018) - [c17]Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour:
Generalized Score Functions for Causal Discovery. KDD 2018: 1551-1560 - [c16]Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. UAI 2018: 1063-1072 - [i7]Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, Dacheng Tao, Kayhan Batmanghelich:
Causal Generative Domain Adaptation Networks. CoRR abs/1804.04333 (2018) - 2017
- [j32]Joseph D. Ramsey, Madelyn Glymour, Ruben Sanchez-Romero
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. 3(2): 121-129 (2017) - [c15]Biwei Huang, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM 2017: 913-918 - [c14]Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017: 1347-1353 - [c13]Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao:
Causal Discovery from Temporally Aggregated Time Series. UAI 2017 - [i6]Andrew J. Sedgewick, Joseph D. Ramsey, Peter Spirtes, Clark Glymour, Panayiotis V. Benos:
Mixed Graphical Models for Causal Analysis of Multi-modal Variables. CoRR abs/1704.02621 (2017) - [i5]Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour:
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. CoRR abs/1706.03768 (2017) - 2016
- [j31]Clark Glymour:
Clark Glymour's responses to the contributions to the Synthese special issue "Causation, probability, and truth: the philosophy of Clark Glymour". Synth. 193(4): 1251-1285 (2016) - [c12]Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf:
Domain Adaptation with Conditional Transferable Components. ICML 2016: 2839-2848 - [c11]Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour:
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI 2016 - 2015
- [j30]Kenneth M. Ford, Patrick J. Hayes
Cognitive Orthoses: Toward Human-Centered AI. AI Mag. 36(4): 5-8 (2015) - [j29]Gregory F. Cooper, Ivet Bahar, Michael J. Becich, Panayiotis V. Benos
The center for causal discovery of biomedical knowledge from big data. J. Am. Medical Informatics Assoc. 22(6): 1132-1136 (2015) - 2014
- [j28]Joseph D. Ramsey, Ruben Sanchez-Romero
Non-Gaussian methods and high-pass filters in the estimation of effective connections. NeuroImage 84: 986-1006 (2014) - 2013
- [j27]Catherine Hanson
Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism. Brain Connect. 3(6): 578-589 (2013) - [j26]Clark Glymour:
Counterfactuals, graphical causal models and potential outcomes: Response to Lindquist and Sobel. NeuroImage 76: 450-451 (2013) - [i4]David Danks, Clark Glymour:
Linearity Properties of Bayes Nets with Binary Variables. CoRR abs/1301.2263 (2013) - [i3]Clark Glymour:
Psychological and Normative Theories of Causal Power and the Probabilities of Causes. CoRR abs/1301.7377 (2013) - 2012
- [j25]Clark Glymour:
On the Possibility of Inference to the Best Explanation. J. Philos. Log. 41(2): 461-469 (2012) - [i2]Frederick Eberhardt, Clark Glymour, Richard Scheines:
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. CoRR abs/1207.1389 (2012) - [i1]Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes:
Learning Measurement Models for Unobserved Variables. CoRR abs/1212.2516 (2012) - 2011
- [j24]Joseph D. Ramsey, Peter Spirtes, Clark Glymour:
On meta-analyses of imaging data and the mixture of records. NeuroImage 57(2): 323-330 (2011) - [j23]Joseph D. Ramsey, Stephen José Hanson, Clark Glymour:
Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage 58(3): 838-848 (2011) - [r1]Frederick Eberhardt, Clark Glymour:
Hans Reichenbach's Probability Logic. Inductive Logic 2011: 357-389 - 2010
- [j22]Carlos Perez, Eman El-Sheikh, Clark Glymour:
Discovering effective connectivity among brain regions from functional MRI data. Int. J. Comput. Heal. 1(1): 86-102 (2010) - [j21]Joseph D. Ramsey, Stephen José Hanson
Six problems for causal inference from fMRI. NeuroImage 49(2): 1545-1558 (2010) - [j20]Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang
Actual causation: a stone soup essay. Synth. 175(2): 169-192 (2010) - [c10]Eman El-Sheikh, Carlos Perez, Clark Glymour:
Using Causal Modeling for Determining Connectivity among Brain Regions. IC-AI 2010: 653-659
2000 – 2009
- 2008
- [j19]Tianjiao Chu, Clark Glymour:
Search for Additive Nonlinear Time Series Causal Models. J. Mach. Learn. Res. 9: 967-991 (2008) - [c9]Robert E. Tillman, David Danks, Clark Glymour:
Integrating Locally Learned Causal Structures with Overlapping Variables. NIPS 2008: 1665-1672 - 2007
- [p1]Clark Glymour:
Trade-Offs. Induction, Algorithmic Learning Theory, and Philosophy 2007: 219-232 - 2006
- [j18]Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes:
Learning the Structure of Linear Latent Variable Models. J. Mach. Learn. Res. 7: 191-246 (2006) - 2005
- [c8]Frederick Eberhardt, Clark Glymour, Richard Scheines:
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. UAI 2005: 178-184 - 2003
- [j17]Tianjiao Chu, Clark Glymour, Richard Scheines, Peter Spirtes:
A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays. Bioinform. 19(9): 1147-1152 (2003) - [c7]Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes:
Learning Measurement Models for Unobserved Variables. UAI 2003: 543-550 - 2002
- [j16]Joseph D. Ramsey, Paul Gazis, Ted Roush, Peter Spirtes, Clark Glymour:
Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition. Data Min. Knowl. Discov. 6(3): 277-293 (2002) - [j15]Jonathan Moody, Ricardo Bezerra de Andrade e Silva, Joseph Vanderwaart, Joseph D. Ramsey, Clark Glymour:
Classification and filtering of spectra: A case study in mineralogy. Intell. Data Anal. 6(6): 517-530 (2002) - 2001
- [c6]David Danks, Clark Glymour:
Linearity Properties of Bayes Nets with Binary Variables. UAI 2001: 98-104 - 2000
- [b1]Peter Spirtes, Clark Glymour, Richard Scheines:
Causation, Prediction, and Search, Second Edition. Adaptive computation and machine learning, MIT Press 2000, ISBN 978-0-262-19440-2, pp. I-XXI, 1-543 - [j14]Clark Glymour:
Android Epistemology For Babies: Relections On Words, Thoughts And Theories. Synth. 122(1-2): 53-68 (2000)
1990 – 1999
- 1999
- [j13]Clark Glymour:
Rabbit Hunting. Synth. 121(1-2): 55-78 (1999) - 1998
- [j12]Clark Glymour, Kenneth M. Ford, Patrick J. Hayes:
Ramón Lull and the Infidels. AI Mag. 19(2): 136 (1998) - [j11]Clark Glymour:
Learning Causes: Psychological Explanations of Causal Explanation. Minds Mach. 8(1): 39-60 (1998) - [j10]Clark Glymour:
Buy and Use Thinking Things Through. Minds Mach. 8(2): 309-310 (1998) - [c5]Clark Glymour:
Psychological and Normative Theories of Causal Power and the Probabilities of Causes. UAI 1998: 166-172 - 1997
- [j9]Kenneth M. Ford, Clark Glymour, Patrick J. Hayes:
On the Other Hand - Cognitive Prostheses. AI Mag. 18(3): 104 (1997) - [j8]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:
An evaluation of machine-learning methods for predicting pneumonia mortality. Artif. Intell. Medicine 9(2): 107-138 (1997) - [j7]Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth
Statistical Themes and Lessons for Data Mining. Data Min. Knowl. Discov. 1(1): 11-28 (1997) - [c4]Thomas S. Richardson, Peter Spirtes, Clark Glymour:
A Note on Cyclic Graphs and Dynamical Feedback Systems. AISTATS 1997: 421-428 - 1996
- [j6]Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth
Statistical Inference and Data Mining. Commun. ACM 39(11): 35-41 (1996) - 1995
- [c3]Clark Glymour:
Available Technology for Discovering Causal Models, Building Bayes Nets, and Selecting Predictors: The TETRAD II Program. KDD 1995: 130-135 - 1994
- [c2]Marek J. Druzdze, Clark Glymour:
Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges. KDD Workshop 1994: 419-430 - 1992
- [j5]Kevin T. Kelly, Clark Glymour:
Inductive inference from theory laden data. J. Philos. Log. 21(4): 391-444 (1992) - 1991
- [j4]Clark Glymour:
The hierarchies of knowledge and the mathematics of discovery. Minds Mach. 1(1): 75-95 (1991) - 1990
- [j3]Kevin T. Kelly, Clark Glymour:
Theory discovery from data with mixed quantifiers. J. Philos. Log. 19(1): 1-33 (1990)
1980 – 1989
- 1985
- [j2]Clark Glymour:
Independence Assumptions and Bayesian Updating. Artif. Intell. 25(1): 95-99 (1985) - 1984
- [c1]Clark Glymour, Richmond H. Thomason:
Default Reasoning and the Logic of Theory Perturbation. NMR 1984: 93-102
1970 – 1979
- 1972
- [j1]Michael Friedman, Clark Glymour:
If quanta had logic. J. Philos. Log. 1(1): 16-28 (1972)
Coauthor Index
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last updated on 2024-12-05 20:46 CET by the dblp team
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