Friday, September 30th, 2022
Mathematics of Interpretable Machine Learning
The last decade has seen a dramatic improvement in the capabilities of machine learning methods and their areas of application have exploded, impacting fields from medical imaging diagnosis and algorithmic trading, to product recommendations and molecular biology. At the same time, the increasing complexity of these models-mostly based on deep artificial neural networks-has rendered them less interpretable: it is difficult to understand what input features, and to what extent, are responsible to produce a certain output.
This mini symposium brings together researchers working on computer science, statistics and related areas, to discuss the fundamental underpinnings of explainable machine learning. These presentations will present the latest results on the mathematical formulation, analysis and guarantees for interpretable predictors.
The Speakers
James Zou
Stanford
Anastasios Angelopoulos
Berkeley
Gitta Kutyniok
Ludwig Maximilian
University of Munich
Mateo Sessia
University of Southern California, Marshall School of Business
Praneeth Netrapalli
Google Research, India
Yaniv Romano
Technion
Jeremias Sulam
Johns Hopkins University
Wesley Tansey
Memorial Sloan Kettering Cancer Center
The Venue
SIAM Conference on Mathematics of Data Science
San Diego, September 26-30, 2022
The Society for Industrial and Applied Mathematics (SIAM) is organizing its second Conference on Mathematics of Data Science. This conference will provide a forum to present work that advances mathematical, statistical, and computational methods in the context of data and information sciences. The conference aims to bring together researchers who are building foundations for data science and its applications across science, engineering, technology, and society.
Sponsors
Code of Conduct
Our mini-symposium is committed to a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, race, ethnicity, religion (or lack thereof), or technology choices, and we abide by SIAM's code of conduct. We encourage all attendees to review this at the following link.