[CMU AI Seminar] November 22 at 12pm (NSH 3305 & Zoom) -- Aldo Pacchiano (Microsoft Research) -- Online Model Selection: the principle of regret balancing -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Reminder that this is happening now.

On Sun, Nov 20, 2022 at 1:44 PM Asher Trockman <ashert at cs.cmu.edu> wrote:

> Dear all,
>
> We look forward to seeing you *this coming Tuesday (11/22)* from *1**2:00-1:00
> PM (U.S. Eastern time)* for the next talk of this semester's
> *CMU AI Seminar*, sponsored by SambaNova Systems <https://sambanova.ai/>.
> The seminar will be held in NSH 3305 *with pizza provided *and will be
> streamed on Zoom.
>
> To learn more about the seminar series or to see the future schedule,
> please visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>
> On 11/22, *Aldo Pacchiano* (Microsoft Research) will be giving a talk
> titled *"**Online Model Selection: the principle of regret balancing**".*
>
> *Title*: Online Model Selection: the principle of regret balancing
>
> *Talk Abstract*: We will introduce the problem of online model selection
> where a learner is to select among a set of online algorithms to solve a
> specific problem instance. We would like to design algorithms that allow
> such a learner to select in an online fashion the best algorithm without
> incurring much regret. This problem is challenging because in contrast with
> for example multi armed bandits, the algorithms' rewards -due to the
> algorithm's own learning process- may be non-stationary. We will introduce
> the principle of regret balancing, a simple, practical and effective model
> selection algorithmic design technique that allows for online selection of
> the best among multiple (base) algorithms in a fully blackbox fashion.
> Regret balancing solves the problem of non-stationarity by introducing an
> elegant `misspecification test' that can efficiently detect when a base
> algorithm is not appropriate for the problem at hand. Regret balancing
> techniques have also been used to provide clarity to some long-standing
> problems in online learning such as corruption learning in MDPs.
>
> *Speaker Bio*: Aldo Pacchiano is a postdoctoral researcher at Microsoft
> Research NYC. He obtained his PhD at UC Berkeley where he was advised by
> Prof. Peter Bartlett and Prof. Michael Jordan. His research lies in the
> areas of Reinforcement Learning, Online Learning, Bandits and Algorithmic
> Fairness. He is particularly interested in furthering our statistical
> understanding of learning phenomena in adaptive environments and use these
> theoretical insights and techniques to design useful algorithms in (among
> other things) bandits, RL, and experimental design.
>
> *In person: *NSH 3305
> *Zoom Link*:
> https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09
>
> Thanks,
> Asher Trockman
>
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