[AI Seminar] AI Lunch -- Aaditya Ramdas(UC Berkeley) -- March 14

Adams Wei Yu weiyu at cs.cmu.edu
Fri Mar 10 11:48:35 EST 2017

Dear faculty and students,

We look forward to seeing you Next Tuesday, March 14, at noon in NSH 3305
for AI lunch. To learn more about the seminar and lunch, please visit
the AI Lunch webpage <http://www.cs.cmu.edu/~aiseminar/>.

On Tuesday, Aaditya Ramdas <http://people.eecs.berkeley.edu/~aramdas/> from
UC Berkeley will give a talk titled *Multi A(rmed)/B(andit) Testing with
online FDR control*.

*Abstract*: We propose a new framework as an alternative to existing setups
for controlling false alarms across multiple A/B tests; it combines ideas
from pure exploration for best-arm identification in multi-armed bandits
(MAB), with online false discovery rate (FDR) control. This framework has
various applications, including pharmaceutical companies testing a control
pill against a few treatment options, to internet companies testing their
current default webpage (control) versus many alternatives (treatment). Our
setup involves running a possibly infinite sequence of best-arm MAB
instances, and controlling the overall FDR of the process in a fully online
manner. Our main contributions are: (i) to propose reasonable definitions
for a null hypothesis; (ii) to demonstrate how one can derive an
always-valid sequential p-value for such a null hypothesis which allows
users to continuously monitor and stop any running MAB instance at any
time; and (iii) to embed MAB instances within online FDR algorithms in a
way that allows setting MAB confidence-levels based on FDR rejection
thresholds. In addition, we adapt existing theory from both the MAB and
online FDR literature to ensure that our framework comes with strong
sample-optimality guarantees, as well as control of the power and (a
modified) FDR at any time. We run extensive simulations to verify our
claims and report results on real data collected from the New Yorker
Cartoon Caption contest.

Joint work with Fan Yang, Kevin Jamieson, Martin Wainwright.

*Bio*: Aaditya Ramdas is a postdoctoral researcher in Statistics and EECS
at UC Berkeley, advised by Michael Jordan and Martin Wainwright. He
finished his PhD in Statistics and Machine Learning at CMU, advised by
Larry Wasserman and Aarti Singh. A lot of his research focuses on modern
aspects of reproducibility in science and technology -- involving
statistical testing and false discovery rate control in static and dynamic
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