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

[Adams Wei Yu]

A gentle reminder that the talk will be tomorrow (Tuesday) noon.

On Fri, Mar 10, 2017 at 11:48 AM, Adams Wei Yu <weiyu at cs.cmu.edu> wrote:

> 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
> settings.
>
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