[Statseminars] Fwd: MINDS Seminar, Thurs. May 21 @ 2:30 Eastern (US), Daniel Spielman (Yale)

[Dan Spielman]

Here's the announcement of a talk I'm giving on Thursday.

  --Dan


---------- Forwarded message ---------
From: Iwen, Mark <iwenmark at msu.edu>
Date: Sun, May 17, 2020 at 7:53 AM
Subject: MINDS Seminar, Thurs. May 21 @ 2:30 Eastern (US), Daniel Spielman
(Yale)
To: <ONEWORLDMINDS at list.msu.edu>


Dear Colleagues,

We are delighted to announce that *Daniel Spielman* will give the next One
World Mathematics of Information, Data, and Signals (MINDS) Seminar
this *Thursday,
May 21st, at 2:30 pm EDT (11:30 am Pacific time)*.  Attendees can watch the
seminar using the following zoom link at that time:

https://msu.zoom.us/j/99831487005

Prof. Spielman's title and abstract are below, and can also be found on the
seminar website along with information about other upcoming talks and more
at https://sites.google.com/view/minds-seminar/home

======================================================
Balancing covariates in randomized experiments using the Gram–Schmidt walk

In randomized experiments, such as a medical trials, we randomly assign the
treatment, such as a drug or a placebo, that each experimental subject
receives.  Randomization can help us accurately estimate the difference in
treatment effects with high probability.  We also know that we want the two
groups to be similar: ideally the two groups would be similar in every
statistic we can measure beforehand. Recent advances in algorithmic
discrepancy theory allow us to divide subjects into groups with similar
statistics.

By exploiting the recent Gram-Schmidt Walk algorithm of Bansal, Dadush,
Garg, and Lovett, we can obtain random assignments of low discrepancy.
These allow us to obtain more accurate estimates of treatment effects when
the information we measure about the subjects is predictive, while also
bounding the worst-case behavior when it is not.

We will explain the experimental design problem we address, the
Gram-Schmidt walk algorithm, and the major ideas behind our analyses.  This
is joint work with Chris Harshaw, Fredrik Sävje, and Peng Zhang.

Paper: https://arxiv.org/abs/1911.03071
Code: https://github.com/crharshaw/GSWDesign.jl
======================================================

Please feel free forward this email to others who may be interested.  If
this email has been forwarded to you and you'd like to receive your own
announcements in the future, you can sign up for them here

https://forms.gle/wYNPom4ZKQF6QoDXA

See you Thursday,

Mark Iwen, Michigan State University (MSU)
Afonso Bandeira, ETH Zurich
Matthew Hirn, Michigan State University (MSU)
Felix Krahmer, Technische Universität München (TUM)
Deanna Needell, University of California, Los Angeles (UCLA)
Rayan Saab, University of California, San Diego (UCSD)
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