[Theory] Re: [Theory Lunch] Jeffrey Negrea, Wednesday 9/28 12:30pm-1:45pm, *JCL 298*

[Antares Chen]

Happening now!

On Tue, Sep 27, 2022 at 8:44 AM Antares Chen <antaresc at uchicago.edu> wrote:

> Hi everyone,
>
> Hope the start of your quarter is going well! Theory Lunch is resuming
> tomorrow *Wednesday 9/28 12:30pm* in *JCL 298*. Jeffrey Negrea will be
> giving our first presentation starting around 1:00pm. It'll last a bit
> longer than usual, ending around 1:45. If you're free, consider stopping by
> and catching up with friends and colleagues in the theory group over some
> lunch!
>
> See you soon,
> Antares
>
> ************************************************************
> **************************
>
> *Date**:* September 28, 2022
> *Time: *12:30pm CT
> *Location: *JCL 298
>
> *Speaker: **Jeffrey Negrea* <https://utstat.toronto.edu/~negrea/>
>
> *Title: **Adapting to failure of the IID assumption*
>
> *Zoom: *[link
> <https://uchicago.zoom.us/j/99171292172?pwd=UlR4ZkY1aXJCdkRBTjJqZGwrR0M0QT09>
> ]
>
> *Abstract:* Assumptions on data are used to develop prediction methods
> with optimistic performance guarantees. Even if these assumptions don’t
> hold, we often believe that if our models are “nearly correct”, then our
> methods will have performance similar to those optimistic guarantees. How
> can we use models that we know to be wrong, but expect to be nearly
> correct, in a way that is robust and reliable? In order to provide
> robustness to the failure of our models, we must quantify the degree to
> which our simplifying models fail to explain observed data, and develop
> prediction methods that adapt to the degree of this failure.
>
> In this seminar, I will discuss my work on the canonical problem of
> sequential prediction with expert advice, i.e., combining predictions from
> a large number of models or experts. We define a continuous spectrum of
> relaxations of the IID assumption, with IID data at one extreme and
> adversarial data at the other. We develop an online learning method that
> adapts to the level of failure of the IID assumption. We quantify the
> difficulty of prediction with expert advice in all scenarios along the
> spectrum we introduce, demonstrate that the prevailing methods do not adapt
> to this spectrum, and present new methods that are adaptively minimax
> optimal. More broadly, this work shows that it is possible to develop
> methods that are both adaptive and robust: they realize the benefits of the
> IID assumption when it holds, without ever compromising performance when
> the IID assumption fails, and without having to know the degree to which
> the IID assumption fails in advance.
>
> This seminar is based on the following two research papers:
> 1. https://arxiv.org/abs/2007.06552
> 2.
> https://proceedings.neurips.cc/paper/2021/hash/dcd2f3f312b6705fb06f4f9f1b55b55c-Abstract.html
>
> [Theory Lunch Webpage
> <https://urldefense.com/v3/__https://orecchia.net/event/theory-lunch/__;!!BpyFHLRN4TMTrA!pwdRh9yLA-IBD6NCNvREJGd9Nj5jtC6_N-AowF6HSwIQeb1FPAmu0L_tAswwp_F5nRs$>
> ]
> [Theory Lunch Calendar
> <https://urldefense.com/v3/__https://calendar.google.com/calendar/u/0/embed?src=c_osgf1c1qemdras8mu7l7pdhjrs@group.calendar.google.com&ctz=America*Chicago__;Lw!!BpyFHLRN4TMTrA!pwdRh9yLA-IBD6NCNvREJGd9Nj5jtC6_N-AowF6HSwIQeb1FPAmu0L_tAsww4XtIRnQ$>
> ]
>
> ************************************************************
> **************************
>
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