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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.
ZOOM LINK: https://bostonu.zoom.us/j/99234972161?pwd=T0pnTVdPRm5TZUJWNEJ1ZHdnVzhMdz09
When | 10:00 am to 11:30 am on Monday, March 22, 2021 |
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Contact Organization | Faculty of Computing & Data Sciences |
Fees | Free |
Speakers | Maggie Makar |