Structure Refinement in First Order Conditional Influence Language Sriraam Natarajan, Weng-Keen Wong, Prasad Tadepalli School of EECS, Oregon State University.

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Structure Refinement in First Order Conditional Influence Language Sriraam Natarajan, Weng-Keen Wong, Prasad Tadepalli School of EECS, Oregon State University Weighted Mean{ If {task(t), doc(d), role(d,r,t)} then t.id, r.id Qinf (Mean) d.folder t.id, r.id Qinf (Mean) d.folder If {doc(s), doc(d), source(s,d) } then s.folder Qinf (Mean) d.folder s.folder Qinf (Mean) d.folder} t1.id d.folder s2.folder d.folder r1.id t2.id r2.id s1.folder W 1 W 2 “Unrolled” Network for Folder Prediction First-order Conditional Influence Language (FOCIL) t1.id d.f s2.f r1.id r2.id t2.cd s1.f t1.cd t2.la s1.la s2.la d.f t1.laT2.id Prior Network d.f s2.f r1.id t2.id r2.id s1.f t1.id d.f Learned Network Prior Program Learned Program Conditional BIC score = -2 *CLL + d m logNConditional BIC score = -2 *CLL + d m logN Different instantiations of the same rule share parameters Different instantiations of the same rule share parameters Conditional Likelihood: EM – Maximize the joint likelihood Conditional Likelihood: EM – Maximize the joint likelihood CBIC score with penalty scaled down CBIC score with penalty scaled down Greedy Search with random restartsGreedy Search with random restarts Scoring metric Folder Prediction RankExhaustive -RHC+RR - RExhaustive - IHC+RR - I Score Synthetic Data Set Irrelevant attributes Relevant attributes Data is expensive – Exploit prior knowledge in structure search Data is expensive – Exploit prior knowledge in structure search Derived the CBIC score for our setting Derived the CBIC score for our setting Learned the “true” network in the synthetic dataset Learned the “true” network in the synthetic dataset Folder dataset: Learned the best network with only relevant attributes Folder dataset: Learned the best network with only relevant attributes Folder dataset with irrelevant attributes: Folder dataset with irrelevant attributes: Conclusions CBIC l earne d < CBIC b es t Different scoring metrics BDeu BDeu Bias/Variance Bias/Variance Choose the best combining rule that fits the data Choose the best combining rule that fits the data Structure refinement in large real-world domains Structure refinement in large real-world domains Future work What is the correct complexity penalty in the presence of multi-valued variables? What is the correct complexity penalty in the presence of multi-valued variables? Counting the # of parameters may not be the right solution Counting the # of parameters may not be the right solution What is the right scoring metric in relational setting for classification? What is the right scoring metric in relational setting for classification? Can the search space be intelligently pruned? Can the search space be intelligently pruned? Issues Weighted Mean{ If {task(t), doc(d), role(d,r,t)} then t.id, r.id Qinf (Mean) d.folder t.id, r.id Qinf (Mean) d.folder If {doc(s), doc(d), source(s,d) } then s.folder Qinf (Mean) d.folder s.folder Qinf (Mean) d.folder} Weighted Mean{ If {task(t), doc(d), role(d,r,t)} then t.id, r.id, t.creationDate, t.lastAccessed Qinf (Mean) d.folder t.id, r.id, t.creationDate, t.lastAccessed Qinf (Mean) d.folder If {doc(s), doc(d), source(s,d) } then s.folder, s.lastAccessed Qinf (Mean) d.folder s.folder, s.lastAccessed Qinf (Mean) d.folder}