Mind Change Optimal Learning Of Bayes Net Structure

1. Mind Change Optimal Learning Of Bayes Net Structure Oliver Schulte School of Computing ScienceSimon Fraser UniversityVancouver, Canada oschulte@cs.sfu.ca with Wei Luo (SFU, wluoa@cs.sfu.ca) andRuss Greiner (U of Alberta, greiner@cs.ualberta.ca)

2. Outline • Brief Intro to Bayes Nets (BNs). • Language Learning Model for BN Structure Learning. • Mind Change Complexity of BN Learning. • Mind Change, Convergence Time Optimality. • NP-hardness of Optimal Learner. Mind Change Optimal Learning of Bayes Net Structure

3. Bayes Nets: Overview • Very widely used graphical formalism for probabilistic reasoning and KR in AI and machine learning. • Bayes Net Structure = Directed Acyclic Graph. • Nodes = Variables of Interest. • Arcs = direct “influence”, “association”. • Structure represents probabilistic conditional dependencies (correlations). Mind Change Optimal Learning of Bayes Net Structure

4. Example of Bayes Net Structure Season Sprinkler Rain Season dependson Slippery. Sprinkler depends on Rain. Sprinkler does not depend on Rain given Season. Sprinkler depends on Rain given Season, Wet. Wet Slippery Mind Change Optimal Learning of Bayes Net Structure

5. A A A Patterns entail Dependencies C Dep(A,B),Dep(A,B|C),Dep(B,C),Dep(B,C|A),Dep(A,C|B) B C Dep(A,B),Dep(A,B|C) B C B Mind Change Optimal Learning of Bayes Net Structure

6. A B A B C C Pattern = DAG Equivalence Class • Write Dep(G) for the dependencies defined by DAG G. • Natural Equivalence relation: G  G’Dep(G) = Dep(G’). • A partially directed graph, called a pattern, represents the equivalence class for a given DAG G. G pattern G’ A B C Mind Change Optimal Learning of Bayes Net Structure

7. Constraint-Based BN Learning as Language Learning Constraint-Based Approach: Learn BN from (in)dependency information. Spirtes, Glymour, Shines (2000); Pearl and Verma (2000); Margaritis and Thrun (1999); Cheng and Greiner (2001). A BN learner maps a sequence of dependencies (repetitions allowed) to a pattern or to ?. Mind Change Optimal Learning of Bayes Net Structure

8. A A C C B B Identification with Bounded Mind Changes • Learner  changes its mind on text T at stage k+1 ,(T[k]) ≠ (T[k+1]) or(T[k]) ≠ ? and (T[k+1]) = ?. • Learner identifies language collection L with k mind changes ,  identifiesL and changes its mind at most k times on any text for a language in L. • L is identifiable with k mind changes , there is a learner  that identifies L with k mind changes. … Text Dep(A,B) Dep(B,C) Dep(A,C|B) … Conjectures ?

9. Occam Learner for Diagrams W M Gender S/W V Type S S V V W W Type Type

10. Geiger Example values = {Worker, Visitor, Spy} values = {Man,Woman} Type Gender values = {true,false} Badge

11. Geiger diagrams p = 0.3 p = 0.7 Woman Man Gender p = 0 p = 1 Spy Visitor Worker Type p = 0.3 p = 0 p = 0.7 p = 1 Spy Visitor Spy Worker Visitor Type Type Woman Man Gender

12. PED partition

13. Inclusion Depth and Mind Change Bounds Proposition (Luo and Schulte 2006)Suppose that L has finite thickness. Then the best mind change bound for L is given by the length of the longest inclusion chain L1 L2 … Lk formed by languages in L. 2,3,4 1,2 2,3 longest inclusion chain has length 4 1 2 Mind Change Optimal Learning of Bayes Net Structure

14. Abstract Hypothesis Space Conjectures of a learner as evidence items accumulate 1,2,3 1,2 1,3 Evidence Items = {1,2,3} 2 Mind Change Optimal Learning of Bayes Net Structure

15. Learner Examples Occam Learner Not strongly mind-change optimal Not efficient for convergence time Occam Learner Mind Change Optimal Learning of Bayes Net Structure

16. Consistency of Causal Graphs Dependency Statements entails Joint Probability Distribution defines Parametrized Causal Graph Causal Graph Structure Conditional Probability Parameters Mind Change Optimal Learning of Bayes Net Structure

17. Mind Change Complexity of BN Learning Let LV be the collection of dependency relations definable by Bayes nets with variables V.Theorem The longest inclusion chain in LV is of length = the number of edges in a complete graph. Mind Change Optimal Learning of Bayes Net Structure

18. A A A A Maximal Length Inclusion Chain C all dependencies B C Dep(A,B),Dep(A,B|C),Dep(B,C),Dep(B,C|A),Dep(A,C|B) B C Dep(A,B),Dep(A,B|C) B C B

19. Mind Change Optimal Learning • Learner is MC-optimal for language collection L, if given any data sequence , the learner identifies L with the best possible mind change bound for the language collection {L: L is in L and consistent with }. • Proposition A BN learner identifying L is MC-optimal , for all dependency sequences , if there is no unique edge-minimal pattern consistent with , then  = ?. Proof follows from general characterization of MC-optimality in Luo and Schulte (2005,2006). Mind Change Optimal Learning of Bayes Net Structure

20. Alternatives: A A A A C C C C B B B B Example of Mind Change Optimal Learner … Text Dep(A,B) Dep(B,C) Dep(A,C|B) … Conjectures ? Mind Change Optimal Learning of Bayes Net Structure

21. A A C C B B Occam Learner for Casual Graphs Dependency Assertions … Dep(A,B) Dep(B,C) Dep(A,C|B) … Conjectures ? Mind Change Optimal Learning of Bayes Net Structure

22. Convergence Time • Convergence Time = number of observed dependencies - important to minimize • Def (Gold) Learner  is uniformly faster than learner  , •  converges at least as fast as  on every text T, and •  converges strictly faster on some text T. Proposition The learner fast is uniformly faster than any other MC-optimal BN learner. Mind Change Optimal Learning of Bayes Net Structure

23. Complexity Analysis A list of dependencies is compactly represented by a dependency oracle. dependency list Dep(A,B) Dep(B,C) Dep(A,C|B) Oracle Q:“Dep(B,C)” yes Oracle Q:“Dep(A,C)” ? Unique k O-cover Given a dependency oracle O, and a bound k, is there a DAG G covering the dependencies in O with ≤k edges s.t. all other DAGs G’ covering the dependencies in O have more edges than G?

24. NP-hardness result TheoremUnique X3set-Cover reduces to Unique k O-Cover. So if P = RP, then UMOC is NP-hard. Basic Idea: Construct a dependency oracle that forces a tree. • Universe: X1,…,Xm. • Sets: C1,..,Cp. • All elements must be dependent on R. R … C1 C2 Cp -1 Cp … X1 X2 X3 Xm -1 Xm Mind Change Optimal Learning of Bayes Net Structure

25. Conclusion • Constraint-based approach to BN learning analyzed as language learning problem. • Mind Change Complexity = , where n is the number of variables. • Number of edges: new intuitive notion of simplicity for a BN, based on learning theory. • Unique fastest mind-change optimal method is NP-hard. Mind Change Optimal Learning of Bayes Net Structure

26. Future Work • Heuristic Implementation of MC-optimal Learner (GES search). • Leads to a new BN learning algorithm with good performance. Mind Change Optimal Learning of Bayes Net Structure

27. References • W. Luo and O. Schulte. Mind change efficient learning. In COLT 2005, pages 398-412. • W. Luo and O. Schulte. Mind change efficient learning. Information and Computation 204:989-1011, 2006. THE END Mind Change Optimal Learning of Bayes Net Structure