[CMU AI Seminar] March 12 at 12pm (GHC 6115 & Zoom) -- Misha Khodak (CMU) -- The long tail of AI: Learning from algorithms and diverse tasks -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you *this Tuesday (3/12)* 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/>.

On this Tuesday (3/12), *Misha Khodak* (CMU) will be giving a talk titled
*"**The long tail of AI: Learning from algorithms and diverse tasks**"*.

*Title*: The long tail of AI: Learning from algorithms and diverse tasks

*Talk Abstract*: Advances in machine learning (ML) have led to skyrocketing
demand across diverse applications beyond vision and text, resulting in
unique theoretical and practical challenges. I develop principled tools for
tackling under-explored and under-resourced ML applications, focusing on
two settings: (1) learning from algorithmic data and (2) automating ML for
diverse tasks. In this talk, I first introduce a general-purpose way to
design and analyze "meta-algorithms" that improve the performance of other
algorithms by training on similar instances. My approach yields the first
provable guarantees for meta-learning gradient descent and a systematic way
to answer a crucial question in the burgeoning field of algorithms with
predictions: where do the predictions come from? This theory leads to an
effective solution to the challenging problem of federated hyperparameter
tuning and to the attainment of near-instance-optimal solver performance
across sequences of linear systems. I will then present a line of work on
automatically extending the benefits of modern ML to diverse data
modalities, especially in healthcare and the sciences. This includes
architecture search methods that find the "right" neural operation for new
modalities and a technique for cross-modal transfer that enables the
fine-tuning of large language models on diverse tasks in genomics,
differential equations, and beyond.

*Speaker Bio:* Misha Khodak is a PhD student in CS at CMU advised by Nina
Balcan and Ameet Talwalkar. He studies foundations and applications of
machine learning, especially meta-learning and algorithm design. Misha is a
recipient of the Facebook PhD Fellowship and CMU’s TCS Presidential
Fellowship, and he has interned at Microsoft Research, Google Research, and
the Lawrence Livermore National Lab.

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

Thanks,
Asher Trockman
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