Details
The importance of relational (structured) data is evident from its increasing presence: WWW, social networks, relational databases, bibliographic networks, organizational networks, biological pathways, and many more. The rich information in relational data gives rise to a wealth of potential patterns that may characterize a network. The ability to describe and detect relational patterns provides powerful support for many applications, including social network analysis, viral marketing, information extraction, drug discovery, computer vision, robotics and many more. In this workshop, we will explore Statistical Relational Learning (SRL) methods that extend machine learning techniques so that they apply to relational domains made up of objects that interrelate. SRL systems employ probability to reason about uncertainty in network structures. They utilize the expressive power of formal logic to represent the full complexity of heterogeneous networks with multiple types of links, nodes, and attributes. In addition to learning about the different formalisms, we will also cover learning and inference algorithms for such models. The workshop will be a 2 day one with the first day focusing on fundamentals of probabilistic graphical models and the second day focusing on the SRL models. Basic knowledge of AI and ML is encouraged. Elements of probability theory (Bayes rule, axioms of probability etc) is required.
Schedule
Day 1: Introduction to Graphical Models
- - Representation
- - Learning
- - Inference
Day 2: Relational Graphical Models
- - Representation
- - Learning
- - Lifted Inference
Bio
Dr. Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas, Dallas and a RBCDSAI Distinguished Fellow at IIT Madras. He was previously an Associate Professor and earlier an Assistant Professor at Indiana University, Wake Forest School of Medicine, a post-doctoral research associate at University of Wisconsin-Madison and had graduated with his PhD from Oregon State University. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He is a AAAI senior member and has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award and the IU trustees Teaching Award from Indiana University. He is the program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He is the chief editor of Frontiers in ML and AI journal, an associate editor of MLJ, JAIR and DAMI journals and is the electronics publishing editor of JAIR.