[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]

Reminder this is happening soon!

On Mon, Mar 11, 2024 at 12:55 PM Asher Trockman <ashert at cs.cmu.edu> wrote:

> 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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