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[theory-seminar] "Palo Alto, We Have a Problem. There Is No Oracle!" – Amin Karbasi (Thu, 17-Mar @ 4:00pm)

Joachim Neu jneu at stanford.edu
Thu Mar 17 11:05:55 PDT 2022


Reminder: this talk will be today at 4pm via Zoom (link below).
Please join us for snacks at 3:30pm in the Grove outside Packard.


On Mon, 2022-03-14 at 09:47 -0700, Joachim Neu wrote:
> Palo Alto, We Have a Problem. There Is No Oracle!
> Amin Karbasi – Professor, Yale University
> Thu, 17-Mar / 4:00pm / Zoom:
> https://stanford.zoom.us/meeting/register/tJckfuCurzkvEtKKOBvDCrPv3McapgP6HygJ
>  
> Please join us for coffee and snacks at 3:30pm in the Grove outside
> Packard (near Bytes' outdoor seating).
> Abstract
> Artificial intelligence is fundamentally about making decisions under
> uncertainty from a massive pool of possibilities, where combinatorial
> techniques have long been central tools. Indeed, many scientific and
> engineering models feature inherently discrete decision
> variables—from phrases in a corpus to objects in an image. Similarly,
> nearly all aspects of the machine learning pipeline involve discrete
> tasks from data summarization and sketching to feature selection and
> model explanation.
> Classically, in order to design optimization methods, we usually
> assume that the objective function is either fully known or
> accessible via an oracle. In many modern applications, however, the
> objectives we aim to optimize should be rather learned, estimated, or
> simulated from data, a process that is subject to stochastic
> fluctuations. Moreover, it has long been known that solutions
> obtained from combinatorial optimization methods can demonstrate
> striking sensitivity to changes in the parameters of the underlying
> problem. So, what are the guarantees of the combinatorial algorithms
> we develop (and teach) when the perfect oracle does not exist? In
> this talk, we will address this challenge and build a fundamentally
> new connection between discrete and (non-convex) continuous
> optimizations that aim to lift the current provable methods out of
> the sterile lab environment and scale them into the real world.
> Bio
> Amin Karbasi is currently an (untenured) associate professor of
> Electrical Engineering, Computer Science, and Statistics & Data
> Science at Yale University. He is also a research scientist at Google
> NY. He has been the recipient of the National Science Foundation
> (NSF) Career Award, Office of Naval Research (ONR) Young Investigator
> Award, Air Force Office of Scientific Research (AFOSR) Young
> Investigator Award, DARPA Young Faculty Award, National Academy of
> Engineering (NAE) Grainger Award, Nokia Bell-Labs Prize, Amazon
> Research Award, Google Faculty Research Award, Microsoft Azure
> Research Award, Simons Research Fellowship, and ETH Research
> Fellowship. His work on machine learning, statistics, and signal
> processing has received awards in a number of premier conferences and
> journals, including Medical Image Computing and Computer-Assisted
> Interventions Conference (MICCAI), Facebook-MAIN award from AI-
> Neuroscience symposium, International Conference on Artificial
> Intelligence and Statistics (AISTATS), IEEE Communications Society
> Data Storage, International Conference on Acoustics, Speech, and
> Signal Processing (ICASSP), ACM SIGMETRICS, and IEEE International
> Symposium on Information Theory (ISIT). His Ph.D. work received the
> Patrick Denantes Memorial Prize for the best doctoral thesis from the
> School of Computer and Communication Sciences at EPFL, Switzerland.
> This talk is hosted by the ISL Colloquium. To receive talk
> announcements, subscribe to the mailing list isl-
> colloq at lists.stanford.edu.
> 
> Mailing list:
> https://mailman.stanford.edu/mailman/listinfo/isl-colloq
> This talk: http://isl.stanford.edu/talks/talks/2022q1/amin-karbasi/
> 
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