PPT-1 Learning Markov Logic

Networks Using Structural Motifs Stanley Kok Dept of Computer Science and Eng University of Washington Seattle USA Joint work with Pedro Domingos Background Learning Using Structural Motifs. Sai. Zhang. , . Congle. Zhang. University of Washington. Presented. . by . Todd Schiller. Software bug localization: finding the likely buggy code fragments. A . software. system. (. source code. Parag. . Singla. Dept. of Computer Science and Engineering. Indian Institute of Technology, Delhi. Joint work with people at . University of Washington and IIT Delhi . Overview. Motivation. Markov logic. Jean-Philippe Pellet. Andre . Ellisseeff. Presented by Na Dai. Motivation. Why structure . l. earning?. What are Markov blankets?. Relationship between feature selection and Markov blankets?. Previous work. Torrey . and Jude Shavlik. University of . Wisconsin. Madison WI, USA. Policy Transfer via. Markov Logic Networks. Background. Approaches for transfer in reinforcement learning. Relational transfer with Markov Logic Networks. Hao. Wu. Mariyam. Khalid. Motivation. Motivation. How would we model this scenario?. Motivation. How would we model this scenario?. Logical Approach. Motivation. How would we model this scenario?. Logical Approach. (Markov Nets). (Slides from Sam . Roweis. ). Connection to MCMC:. . . MCMC requires sampling a node given its . markov. blanket. . Need to use P(. x|MB. (x)). . . For . Bayes. nets MB(x) contains more. Logic and Probability. Parag Singla. Dept. of Computer Science & Engineering. Indian Institute of Technology Delhi. Overview. Motivation & Background. Markov logic. Inference & Learning. Abductive. Model Definition. Comparison to Bayes Nets. Inference techniques. Learning Techniques. A. B. C. D. Qn. : What is the. . most likely. . configuration of A&B?. Factor says a=b=0. But, marginal says. Perceptron. SPLODD. ~= AE* – 3, 2011. * Autumnal Equinox. Review. Computer science is full of . equivalences. SQL .  relational algebra. YFCL optimizing … on the training data. g. cc. –O4 . Fehringer. Seminar: Probabilistic Models for Information Extraction. by Dr. Martin . Theobald. and Maximilian . Dylla. . Based on Richards, M., and . Domingos. , P. (2006). Markov Logic Networks. 1. Tushar. . Khot. Joint work with . Sriraam. . Natarajan. , . Kristian. . Kersting. and . Jude . Shavlik. Sneak Peek. Present a method to learn structure and parameter for MLNs . simultaneously. Use functional gradients to learn many . Parag. . Singla. & Raymond J. Mooney. Dept. of Computer Science. University of Texas, Austin. Motivation . [ Blaylock & Allen 2005] . Road Blocked!. Road Blocked!. Heavy Snow; Hazardous Driving. Relational. . Learning. . for. . NLP. William. . Y.. . Wang. William W. Cohen. Machine Learning Dept . and Language Technologies. . Inst.. joint work with:. Kathryn Rivard Mazaitis. Outline. Motivation. in Markov Logic using an RDBMS. Feng . Niu. , Chris . Ré. , . AnHai. Doan, and Jude . Shavlik. University of Wisconsin-Madison. One Slide Summary. 2. Machine Reading . is a DARPA program to capture knowledge expressed in free-form text.