Machine Learning and Causality: Building Efficient, Reliable Models for Decision-Making

Speaker: Maggie Makar, CSAIL MIT

Current techniques for causal inference typically rely on having access to large amounts of data, limiting their applicability to data-constrained settings. In addition, evidence has shown that most predictive models are insufficiently robust with respect to shifts at test time. Makar will present her work on building novel techniques addressing these problems.


When 10:00 am to 11:30 am on Monday, March 22, 2021
Contact Organization Faculty of Computing & Data Sciences
Fees Free
Speakers Maggie Makar