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[theory-seminar] "Approximating cross-validation: guarantees for model assessment and selection" – Ashia Wilson (Thu, 18-Mar @ 4:30pm)
tavorb at stanford.edu
Thu Mar 18 10:36:43 PDT 2021
Reminder: this talk is today at 4:30pm.
On Fri, Mar 12, 2021 at 4:48 PM Tavor Baharav <tavorb at stanford.edu> wrote:
> Approximating cross-validation: guarantees for model assessment and
> selectionAshia Wilson – Professor, MIT
> Thu, 18-Mar / 4:30pm / Zoom:
> *To avoid Zoom-bombing, we ask attendees to sign in via the above URL to
> receive the Zoom meeting details by email.*
> Cross-validation (CV) is the de facto standard for selecting accurate
> predictive models and assessing model performance. However, CV suffers from
> a need to repeatedly refit a learning procedure on a large number of
> training datasets. To reduce the computational burden, a number of works
> have introduced approximate CV procedures that simultaneously reduce
> runtime and provide model assessments comparable to CV when the prediction
> problem is sufficiently smooth. An open question however is whether these
> procedures are suitable for model selection. In this talk, I’ll describe
> (i) broad conditions under which the model selection performance of
> approximate CV nearly matches that of CV, (ii) examples of prediction
> problems where approximate CV selection fails to mimic CV selection, and
> (iii) an extension of these results and the approximate CV framework more
> broadly to non-smooth prediction problems like L1-regularized empirical
> risk minimization.
> Ashia is an Assistant Professor in EECS at MIT. Her research focuses on
> the methodological foundations and theory of various topics in machine
> learning. She is interested in developing frameworks for algorithmic
> assessment and providing rigorous guarantees for algorithmic performance.
> She received her BA from Harvard University with a concentration in applied
> mathematics and a minor in philosophy, and a PhD from UC Berkeley in
> statistics. She most recently held a postdoctoral position in the machine
> learning group at Microsoft Research, New England.
> *This talk is hosted by the ISL Colloquium
> <https://isl.stanford.edu/talks/>. To receive talk announcements, subscribe
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> Mailing list: https://mailman.stanford.edu/mailman/listinfo/isl-colloq
> This talk: http://isl.stanford.edu/talks/talks/2021q1/ashia-wilson/
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