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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
Thu Sep 30 10:33:54 PDT 2021


Gentle reminder that this talk is today at 4:45pm in Packard 101.  Please
join us at 4:15pm at the tables outside Packard for coffee and snacks.

On Mon, Sep 27, 2021 at 11:22 AM Tavor Baharav <tavorb at stanford.edu> wrote:

> 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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