International Conference on Machine Learning 2005 - Bonn

Statistical Relational Learning - Part 2

author:Lise Getoor, University of Maryland
published: Feb. 25, 2007, recorded: August 2005, views: 86

Slides

Slides
0:00	Four SRL Approaches
0:43	Frame-based Approaches
1:44	Four SRL Approaches
2:20	Relational Schema
2:55	Probabilistic Relational Model
3:44	Probabilistic Relational Model
4:29	Probabilistic Relational Model
4:47	Relational Skeleton
6:22	PRM w/ Attribute Uncertainty
7:31	A Portion of the BN
8:12	A Portion of the BN
8:46	PRM: Aggregate Dependencies
9:22	PRM: Aggregate Dependencies
10:08	PRM with AU Semantics
12:10	Four SRL Approaches
12:18	PRM Inference
12:48	Inference Example
13:15	PRM Inference: Constructed BN
13:35	PRM Inference
13:54	PRM Inference: Interfaces
14:18	PRM Inference: Interfaces
14:46	PRM Inference: Encapsulation
15:19	PRM Inference: Reuse
15:28	Structured Variable Elimination
16:08	Structured Variable Elimination
16:15	Structured Variable Elimination
16:42	Structured Variable Elimination
16:56	Structured Variable Elimination
16:59	Structured Variable Elimination
17:30	Structured Variable Elimination
17:37	Structured Variable Elimination
17:39	Structured Variable Elimination
18:01	Benefits of SVE
18:46	Limitations of SVE
19:35	Four SRL Approaches
19:50	Learning PRMs w/ AU
20:09	ML Parameter Estimation
21:11	ML Parameter Estimation
22:03	Structure Selection
22:29	Structure Selection
22:39	Legal Models
24:00	Attribute Stratification
26:54	Structure Selection
27:14	Structure Selection
27:21	Searching Model Space
28:25	Phased Structure Search
29:29	Phased Structure Search
29:43	Four SRL Approaches
30:24	Reminder: PRM w/ AU Semantics
31:00	Kinds of structural uncertainty
31:42	Structural Uncertainty
32:26	Citation Relational Schema
32:46	Attribute Uncertainty
33:12	Reference Uncertainty
34:13	PRM w/ Reference Uncertainty
35:31	Reference Uncertainty Example
37:38	Reference Uncertainty Example
39:02	Introduce Selector RVs
40:41	PRMs w/ RU Semantics
41:35	Learning
41:52	Legal Models
42:54	Legal Models
44:27	Structure Search
44:53	Structure Search: New Operators
45:04	Structure Search: New Operators
45:37	PRMs w/ RU Summary
46:15	Existence Uncertainty
46:49	PRM w/ Exists Uncertainty
47:06	Exists Uncertainty Example
47:59	Introduce Exists RVs
48:39	Introduce Exists RVs
50:10	PRMs w/ EU Semantics
50:53	Learning
52:05	Four SRL Approaches
52:33	Learning PRM-CHs
52:57	Learning
53:17	Guaranteeing Acyclicity w/ Subclasses
54:56	Learning PRM-CH
55:30	Learning Class Hierarchy
55:39	PRMs w/ Class Hierarchies
55:43	Summary: Directed Frame-based Approaches
56:34	Four SRL Approaches

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Description

Statistical machine learning is in the midst of a "relational revolution". After many decades of focusing on independent and identically-distributed (iid) examples, many researchers are now studying problems in which the examples are linked together into complex networks. These networks can be a simple as sequences and 2-D meshes (such as those arising in part-of-speech tagging and remote sensing) or as complex as citation graphs, the world wide web, and relational data bases.