CIKM-2013 Tutorial on Statistical Relational Learning

Goals and Summary

Statistical relational learning (SRL) focuses on learning when samples are non-i.i.d. (independent and identically distributed). Domains where data is non-i.i.d. are widespread; examples include Web search, information extraction, perception, medical diagnosis/epidemiology, molecular and systems biology, social science, security, ubiquitous computing, and others. In all of these domains, modeling dependencies between examples can greatly improve predictive performance, and lead to better understanding of the relevant phenomena. However, doing this can be much more complex than treating examples independently. The goal of this tutorial is to provide researchers and practitioners with the tools needed to learn from interdependent examples with no more difficulty than they learn from isolated examples today. There have been a number of previous tutorials on SRL. This tutorial differs from them in a number of ways:


Pedro Domingos is Professor of Computer Science and Engineering at the University of Washington. His research interests are in artificial intelligence, machine learning and data mining. He received a PhD in Information and Computer Science from the University of California at Irvine, and is the author or co-author of over 200 technical publications. He is member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. He was program co-chair of KDD-2003 and SRL-2009, and has served on numerous program committees. He is a AAAI Fellow, and has received several awards, including a Sloan Fellowship, an NSF CAREER Award, a Fulbright Scholarship, an IBM Faculty Award, and best paper awards at several leading conferences. He has carried out extensive research in the tutorial area, and served on the program committees of most SRL and statistical relational AI workshops to date. He has taught several graduate and undergraduate courses in AI and related topics, including courses at Carnegie Mellon University and the University of Washington in the specific area of the tutorial.


The tutorial will be composed of three parts: