2016 Cheriton Research Symposium

10 a.m. - 3 p.m.

DC 1302 - Mark Giesbrecht — Welcome and Opening Remarks

10:30 a.m. -
11:30 a.m.

DC 1302 - Pascal Poupart — Leveraging Data Science to Directly Learn Tractable Models for Probabilistic Inference and Decision Making

Most problems in artificial intelligence are NP-hard and therefore a significant amount of work has been devoted to exploit problem-specific structure or design approximate algorithms with good scalability properties. With the rise of data science, a paradigm shift is taking place. Instead of having a domain expert specify a model for which reasoning is intractable, machine learning allows us to learn tractable models directly from data. The reality is that most models specified by domain experts are approximate while models obtained from data can be more accurate. Furthermore, by employing a hierarchy of models that equate representation complexity with reasoning complexity, it is possible to learn models whose complexity increases with the amount of data and therefore remain tractable. For example, inference in probabilistic graphical models such as Bayesian networks and Markov networks is #P-complete. However if we estimate sum-product networks (equivalent to Bayesian and Markov networks) directly from data, exact probabilistic inference can be done in linear time with respect to the size of the network. In this talk, I will explain how to learn sum-product networks from large streaming datasets in an online and distributed fashion for a wide range of applications including language modelling, handwriting recognition, recommender systems, communication networks, classification and time-series prediction. I will also explain how to learn extensions of sum-product networks for tractable decision making in various diagnosis tasks. 

12 p.m. -
1 p.m.

DC 1302 - David Cheriton - Teaching Computational (Systems) Thinking and Computer Systems in the Real World

DC 1302 - Michael Godfrey - Does your mother know you're here? Understanding software artifact provenance

"Provenance" is a term from archaeology and the arts that refers to a set of evidence supporting the claimed origin of an artifact, such as a piece of pottery or an oil painting. Recently, the term has been used in an electronic context --- "digital provenance" --- to indicate an artifact such as a software component or set of data, really is what it claims to be and should be permitted to be used within sensitive operating environments.