Learning from Networks of Examples

Most machine learning algorithms assume that examples are independent of each other, but many (or most) domains violate this assumption. For example, in real markets customers' buying decisions are influenced by their friends and acquaintances, but data mining for marketing ignores this (as does traditional economics). In this talk I will describe how we can learn models that account for example dependences, and use them to make better decisions. For example, in the marketing domain we are able to pinpoint the most influential customers, "seed" the network by marketing to them, and unleash a wave of word of mouth. We mine these models from collaborative filtering systems and knowledge-sharing Web sites, and show that they are surprisingly robust to imperfect knowledge of the network. I will also survey other applications of learning from networks of examples we are working on, including: combining link and content information in Google-style Web search; automatically translating between ontologies on the Semantic Web; and predicting the evolution of scientific communities.

BIO: Pedro Domingos is an assistant professor in the Department of Computer Science and Engineering at the University of Washington in Seattle. 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 80 technical publications in the fields of large-scale machine learning, probabilistic learning, model ensembles, model selection, cost-sensitive learning, multistrategy learning, adaptive user interfaces, Web search, data integration, anytime reasoning, computer graphics, and others. He is associate editor of JAIR, a member of the editorial board of the Machine Learning journal, and a co-founder of the International Machine Learning Society. He is program co-chair of KDD-2003, and has served on numerous program committees. He has received several awards, including an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, and best paper awards at KDD-98 and KDD-99.