[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]

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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/ai-seminar-announce/attachments/20221120/be66deda/attachment.html>