Learning Models that Predict Objective, Actionable Labels
Russ Greiner
February 23, 2024, Friday, 2:00 PM - 3:00 PM EST
Many medical researchers want a tool that "does what a top medical clinician does but does it better". This presentation explores this goal. This requires first defining what "better" means, leading to the idea of outcomes that are "objective" and then to ones that are actionable, with a meaningful evaluation measure. We will discuss some of the subtle issues in this exploration - what does "objective" mean, the role of the (perhaps personalized) evaluation function, multi-step actions, counterfactual issues, distributional evaluations, etc. Collectively, this analysis argues we should learn models whose outcome labels are objective and actionable, as that will lead to tools that are useful and cost-effective.
After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta, where he is now a Professor in Computing Science (Adjunct in Psychiatry) and the founding Scientific Director of the Alberta Machine Intelligence Institute. He has been Program/Conference Chair for various major conferences, and has served on the editorial boards of a number of other journals. He was elected a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence), was awarded a McCalla Professorship and a Killam Annual Professorship; received a 2020 FGSR Great Supervisor Award and in 2021, received the CAIAC Lifetime Achievement Award and became a CIFAR AI Chair. In 2022, the Telus World of Science museum honored him with a panel, and he received the (UofA) Precision Health Innovator Award, then in 2023, he received the CS-Can | Info-Can Lifetime Achievement Award. For his mentoring, he received a 2020 FGSR Great Supervisor Award, then in 2023, the Killam Award for Excellence in Mentoring. He has published over 300 refereed papers, most in the areas of machine learning and recently medical informatics, including 6 that have been awarded Best Paper prizes. The main foci of his current work are (1) bio- and medical- informatics; (2) survival prediction and (3) formal foundations of learnability.