[CMU AI Seminar] April 11 at 12pm (GHC 6115 & Zoom) -- Lucio Dery (CMU) -- An automated transfer learning approach to tackling learning under limited data -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you *this Tuesday (4/11)* 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 GHC 6115 *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/>.

This Tuesday (4/11), *Lucio Dery* (CMU) will be giving a talk titled *"**An
automated transfer learning approach to tackling learning under limited
data**".*

*Title*: An automated transfer learning approach to tackling learning under
limited data

*Talk Abstract*: Transfer learning is arguably the engine of the current
deep learning revolution in machine learning.  A common branch of transfer
learning is learning with auxiliary objectives — supplementary learning
signals that are introduced to help aid learning on data-starved or highly
complex end-tasks. Whilst much work has been done to formulate useful
auxiliary objectives, their construction is still an art which proceeds by
slow and tedious hand-design. Intuition for how and when these objectives
improve end-task performance has also had limited theoretical backing.

In this talk, I will present a task agnostic approach for automatically
generating a suite of auxiliary objectives and maximally utilizing them to
benefit a specified end-task. We achieve this by deconstructing
existing objectives within a novel unified taxonomy, identifying
connections between them, and generating new ones based on the uncovered
structure.  We theoretically formalize widely-held intuitions about how
auxiliary learning improves generalization on the end-task which leads us
to a principled and efficient algorithm for searching the space of
generated objectives to find those most useful to a specified end-task.

*Speaker Bio:* Lucio Dery is a PhD student in the Computer Science
Department at Carnegie Mellon University co-advised by Ameet Talwalkar and
Graham Neubig. Before starting his PhD, he was a Research Engineer at
Facebook AI Research (FAIR) in Seattle. His current research interests
broadly cover all things related to learning from multiple tasks: transfer
learning, meta-learning, multi-tasking and auxiliary learning. He primarily
explores these fields in the context of Natural Language Processing but the
tools he develops are domain agnostic.

*In person: *GHC 6115
*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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