[ABE-L] Insper Data Science Seminar - In Person

[Hedibert Lopes]

Title: Split conformal prediction and its extensions to non-exchangeable
data.


Speaker: Paulo Orenstein (https://w3.impa.br/~pauloo/)


University: IMPA


Abstract:  Machine learning algorithms offer state-of-the-art predictive
performance in a variety of domains, but often lack an associated measure
of uncertainty quantification. Split conformal prediction is a leading tool
to obtain predictive intervals with virtually no assumptions beyond data
exchangeability. This crucial assumption, however, hinders its
applicability to many important data, such as time series and spatially
dependent processes. In this talk, we will introduce split CP and show how
it can be extended to non-exchangeable settings through a small coverage
penalty. The proposed framework, based on concentration of measure
inequalities, works more generally than traditional split CP, and
experiments corroborate our coverage guarantees even under highly dependent
data.




May 30, 2022

12:00 p.m. de São Paulo, Brasil (UTC/GMT -03:00)

Paulo Renato de Souza room, 2nd floor - Building 1
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