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[theory-seminar] "Sequential Decision Making: How Much Adaptivity Is Needed Anyways?" – Amin Karbasi (Thu, 30-Sep @ 4:45pm)

Tavor Baharav tavorb at stanford.edu
Mon Sep 27 11:22:21 PDT 2021


Sequential Decision Making: How Much Adaptivity Is Needed Anyways?Amin
Karbasi – Professor, Yale

Thu, 30-Sep / 4:45pm / Packard 101

Note: This talk will be held *in person* in Packard 101, and has been
pushed back to 4:45pm to accommodate the new class schedule. The talk will
be streamed on Zoom for those who cannot attend:
https://stanford.zoom.us/meeting/register/tJckfuCurzkvEtKKOBvDCrPv3McapgP6HygJ
 .

Please join us for a coffee half hour starting at 4:15pm at the Bytes
outdoor tables outside of Packard.
Abstract

Adaptive stochastic optimization under partial observability is one of the
fundamental challenges in artificial intelligence and machine learning with
a wide range of applications, including active learning, optimal
experimental design, interactive recommendations, viral marketing,
Wikipedia link prediction, and perception in robotics, to name a few. In
such problems, one needs to adaptively make a sequence of decisions while
taking into account the stochastic observations collected in previous
rounds. For instance, in active learning, the goal is to learn a classifier
by carefully requesting as few labels as possible from a set of unlabeled
data points. Similarly, in experimental design, a practitioner may conduct
a series of tests in order to reach a conclusion. Even though it is
possible to determine all the selections ahead of time before any
observations take place (e.g., select all the data points at once or
conduct all the medical tests simultaneously), so-called a priori
selection, it is more efficient to consider a fully adaptive procedure that
exploits the information obtained from past selections in order to make a
new selection. In this talk, we introduce semi-adaptive policies, for a
wide range of decision-making problems, that enjoy the power of fully
sequential procedures while performing exponentially fewer adaptive rounds.
Bio

Amin Karbasi is currently an associate professor of Electrical Engineering,
Computer Science, and Statistics 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 2019, Office of Naval Research (ONR) Young
Investigator Award 2019, Air Force Office of Scientific Research (AFOSR)
Young Investigator Award 2018, DARPA Young Faculty Award 2016, National
Academy of Engineering Grainger Award 2017, Amazon Research Award 2018,
Google Faculty Research Award 2016, Microsoft Azure Research Award 2016,
Simons Research Fellowship 2017, and ETH Research Fellowship 2013. His work
has also been recognized with a number of paper awards, including Medical
Image Computing and Computer-Assisted Interventions Conference (MICCAI)
2017, International Conference on Artificial Intelligence and Statistics
(AISTAT) 2015, IEEE ComSoc Data Storage 2013, International Conference on
Acoustics, Speech, and Signal Processing (ICASSP) 2011, ACM SIGMETRICS
2010, and IEEE International Symposium on Information Theory (ISIT) 2010
(runner-up). His Ph.D. thesis received the Patrick Denantes Memorial Prize
2013 from the School of Computer and Communication Sciences at EPFL,
Switzerland.

*This talk is hosted by the ISL Colloquium
<https://isl.stanford.edu/talks/>. To receive talk announcements, subscribe
to the mailing list isl-colloq at lists.stanford.edu
<https://mailman.stanford.edu/mailman/listinfo/isl-colloq>.*
------------------------------

Mailing list: https://mailman.stanford.edu/mailman/listinfo/isl-colloq
This talk: http://isl.stanford.edu/talks/talks/2021q4/amin-karbasi/
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