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Learning equivalence classes of directed acyclic latent variable models from multiple datasets with overlapping variables, incl. discussion by Ricardo Silva
Published on 2011-05-063917 Views
While there has been considerable research in learning probabilistic graphical models from data for predictive and causal inference, almost all existing algorithms assume a single dataset of i.i.d
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Presentation
Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables00:00
Learning from single i.i.d. dataset00:13
Learning from multiple datasets with overlapping variables00:59
Examples: Learning neural cascades during cognitive tasks01:54
Formal Problem Statement (1)03:37
Formal Problem Statement (2)03:56
Formal Problem Statement (3)04:23
Formal Problem Statement (4)04:28
Formal Problem Statement (6)04:40
Errors due to latent variables (1)05:32
Errors due to latent variables (2)05:55
Errors due to latent variables (3)06:06
Errors due to latent variables (4)06:26
Maximal Ancestral Graphs (1)06:50
Maximal Ancestral Graphs (2)07:30
Maximal Ancestral Graphs (3)07:53
Maximal Ancestral Graphs (4)08:21
Maximal Ancestral Graphs (5)08:37
Markov Equivalence and PAGs (1)10:48
Markov Equivalence and PAGs (2)11:45
Restated Goal12:54
Related Approach: ION Algorithm (1)13:59
Related Approach: ION Algorithm (2)15:11
Related Approach: ION Algorithm (3)15:38
Related Approach: ION Algorithm (4)15:50
Related Approach: ION Algorithm (5)16:41
Related Approach: ION Algorithm (6)17:09
Related Approach: ION Algorithm (7)17:17
Conditional Independence Testing with Multiple Datasets (1)17:40
Conditional Independence Testing with Multiple Datasets (2)17:54
Conditional Independence Testing with Multiple Datasets (3)18:09
Conditional Independence Testing with Multiple Datasets (4)18:27
Conditional Independence Testing with Multiple Datasets (5)18:45
Conditional Independence Testing with Multiple Datasets (6)18:49
Conditional Independence Testing with Multiple Datasets (7)19:06
Conditional Independence Testing with Multiple Datasets (8)19:24
The Integration of Overlapping Datasets (IOD) Algorith (1)19:53
The Integration of Overlapping Datasets (IOD) Algorith (2)20:18
The Integration of Overlapping Datasets (IOD) Algorith (3)20:37
The Integration of Overlapping Datasets (IOD) Algorith (4)21:11
The Integration of Overlapping Datasets (IOD) Algorith (5)21:34
Removing edges and adding orientations (1)21:58
Removing edges and adding orientations (2)22:15
Removing edges and adding orientations (3)22:39
Removing edges and adding orientations (4)22:44
Removing edges and adding orientations (5)23:33
Removing edges and adding orientations (6)24:16
Removing edges and adding orientations (7)24:50
Removing edges and adding orientations (8)24:54
Example (1)25:14
Example (2)25:30
Example (3)25:38
Example (4)25:51
Example (5)26:20
Correctness and Completeness (1)26:34
Correctness and Completeness (2)26:49
Correctness and Completeness (3)26:59
Simulations27:11
Simulations - 2 Datasets, |V| = 1427:42
Simulations - 3 Datasets, |V| = 1428:06
Application: Learning neural cascades during cognitive tasks28:10
Conclusion (1)28:54
Conclusion (2)29:07
Conclusion (3)29:16
Conclusion (4)29:27
Conclusion (5)29:31
Conclusion (6)29:34
Conclusion (7)29:38
Conclusion (8)29:47
Discussion of “Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables”29:59
On overlapping variables and partial information30:20
Built-in robustness31:10
On selection bias31:55
Beyond independence constraints (1)32:33
Beyond independence constraints (2)33:07
The Bayesian approach33:44
Related problems: finding substructure by generalizing penalized composite likelihood?34:13
Other approaches: generalizing penalized composite likelihood?34:44