Prof. Scott Sanner
Tutorial: Symbolic Methods for Hybrid Inference, Optimization, and Decision-making
To date, our ability to perform exact closed-form inference or optimization in hybrid domains (that is, containing mixed discrete and continuous variables) is largely limited to special well-behaved cases. This tutorial argues that with an appropriate representation and data structure, we can vastly expand the class of models for which we can perform exact, closed-form inference.
In this tutorial, I review the algebraic decision diagram (ADD) and introduce an extension to continuous variables -- termed the extended ADD (XADD) -- to represent arbitrary piecewise functions over discrete and continuous variables and show how to efficiently compute elementary arithmetic operations, integrals, and maximization for these functions. Then I cover a wide range of novel applications where the XADD may be applied: (1) exact learning and inference in expressive discrete and continuous variable graphical models, (2) factored, parameterized linear and quadratic optimization, and (3) exact solutions to continuous state, action, and observation MDPs and POMDPs.
Scott Sanner is an Assistant Professor in Industrial Engineering and Cross-appointed in Computer Science. Previously Scott was an Assistant Professor at Oregon State University and before that he was a Principal Researcher at National ICT Australia (NICTA) and Adjunct Faculty at the Australian National University. Scott earned a PhD in Computer Science from the University of Toronto (2008), an MS in Computer Science from Stanford University (2002), and a double BS in Computer Science and Electrical and Computer Engineering from Carnegie Mellon University (1999).
Scott’s research spans a broad range of topics from the data-driven fields of Machine Learning and Information Retrieval to the decision-driven fields of Artificial Intelligence and Operations Research. Scott has applied the analytic and algorithmic tools from these fields to diverse application areas such as recommender systems, interactive text visualization, and Smart Cities applications including transport optimization.
Scott has served as Program Co-chair for the 26th International Conference on Automated Planning and Scheduling (ICAPS), member of the Editorial Board for the Artificial Intelligence Journal (AIJ) and the Machine Learning Journal (MLJ), and Electronic Editor for the Journal of Artificial Intelligence Research (JAIR). Scott was a co-recipient of the 2014 AIJ Prominent Paper Award and the 2016 Kikuchi-Karlaftis Best Paper Award of the Transport Research Board.
Snacks and refreshments will be provided. Hope to see you there!