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From Kernels to Causality
Published on Aug 26, 20135853 Views
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Chapter list
From Kernels to Causality00:00
Early Kernel Methods01:02
Support-Vector Networks - 102:27
Support-Vector Networks - 203:10
Extracting Support Data for a Given Task - 103:58
Extracting Support Data for a Given Task - 204:15
Kernel PCA05:13
Spectral clustering10:49
Link to kernel PCA10:57
Kernel mean embedding methods - 111:04
Kernel mean embedding methods - 211:48
Kernel mean embedding methods - 314:12
The mean map for samples15:02
Witness function16:54
The mean map for measures - 117:47
The mean map for measures - 219:21
The mean map for measures - 321:33
Two-sample problem22:18
Kernel Independence Testing22:59
Shift-Invariant Optical Realization24:04
Kernels as Green’s Functions28:53
Non-Injectivity of Fourier Imaging - 130:19
Non-Injectivity of Fourier Imaging - 231:56
Algorithmic Method33:31
Two papers35:34
Shortcomings of Machine Learning36:18
Amazon´s recommender36:52
Statistical Implications of Causality38:16
Functional Causal Model39:18
Twilight of the idols42:09
Restricting the Functional Model42:53
Causal Inference with Additive Noise, 2-Variable Case44:54
Identifiability Result45:43
Alternative View46:36
Causal Inference Method46:38
Experiments - 146:51
Experiments - 247:12
Independence-based Regression47:28
Independence of input and mechanism48:21
Inferring deterministic causal relations49:03
Causal independence implies anticausal dependence - 150:02
Causal independence implies anticausal dependence - 251:17
80 Cause-Effect Pairs51:23
80 Cause-Effect Pairs - Examples51:32
Methods51:34
Causal Learning and Anticausal Learning52:30
Covariate Shift and Semi-Supervised Learning53:45
Semi-Supervised Learning55:11
SSL Book Benchmark Datasets55:41
UCI Datasets used in SSL benchmark55:55
Datasets, co-regularized LS regression55:56
Benchmark Datasets55:57
Self-training does not help for causal problems55:58
Co-regularization helps for the anticausal problems56:39
Co-regularizarion hardly helps for the causal problems57:02
From Ordinary Differential Equations to Structural Causal Models57:11
Thank you57:24