Speaker: Marcelo Arenas
Location: Soda 380
Date: November 15, 2023
Time: 11AM – 12PM PST
Abstract:
In recent years, there has been a growing interest in developing methods to explain individual predictions made by machine learning models. This has led to the development of various notions of explanation and scores to justify a model’s classification. However, instead of struggling with the increasing number of such notions, one can turn to an old tradition in databases and develop a declarative query language for interpretability tasks, which would allow users to specify and test their own explainability queries. Not surprisingly, logic is a suitable declarative language for this task, as it has a well-understood syntax and semantics, and there are many tools available to study its expressiveness and the complexity of the query evaluation problem. In this talk, we will discuss our work on developing such a logic for model interpretability.
Bio:
Marcelo Arenas is a Professor at the Department of Computer Science and the Institute for Mathematical and Computational Engineering, at Pontificia Universidad Católica de Chile. He is a Distinguished Member of the Association for Computing Machinery (ACM). Marcelo Arenas received his Ph.D. from the University of Toronto in 2005. His research interests lie in the areas of data management, and the applications of logic in computer science. He has been honored with an IBM Ph.D. Fellowship (2004), a SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention in 2006 for his Ph.D. dissertation “Design Principles for XML Data”, the 2016 Semantic Web Science Association (SWSA) Ten-Year Award for the article “Semantics and Complexity of SPARQL”, and nine best paper awards in various conferences. Marcelo Arenas has served on multiple program committees and editorial boards, and chaired the program committees of ICDT 2015, ISWC 2015, and PODS 2018.