[Theory] REMINDER: 2/2 Talks at TTIC: Ainesh Bakshi, Carnegie Mellon University

[Mary Marre]

*When:*      Wednesday, February 2nd at* 11:30 am CT*



*Where:*     Zoom Virtual Talk (*register in advance here*
<https://uchicagogroup.zoom.us/webinar/register/WN_J_gBUwZZS5yH_x0S7eSTmw>)


*Who: *       Ainesh Bakshi, Carnegie Mellon University

*Title:*        Analytic Techniques for Robust Algorithm Design

*Abstract:*
Modern machine learning relies on algorithms that fit expressive models to
large datasets. While such tasks are easy in low dimensions, real-world
datasets are truly high-dimensional. Additionally, a prerequisite to
deploying models in real-world systems is to ensure that their behavior
degrades gracefully when the modeling assumptions no longer hold.
Therefore, there is a growing need for *efficient algorithms* that fit
reliable and robust models to data.

In this talk, I will provide an overview of designing such efficient and
robust algorithms, with provable guarantees, for fundamental tasks in
machine learning and statistics. In particular, I will describe two
complementary themes arising in this area: *high-dimensional robust
statistics* and *fast numerical linear algebra*. The first addresses how to
fit expressive models to high-dimensional datasets in the presence of
outliers and the second develops fast algorithmic primitives to reduce
dimensionality and de-noise large datasets. I will focus on recent results
on robustly learning mixtures of arbitrary Gaussians and describe the new
algorithmic ideas obtained along the way. Finally, I will make the case for
analytic techniques, such as convex relaxations, being the natural choice
for robust algorithm design.

*Host:* *Yury Makarychev <yury at ttic.edu>*

Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Wed, Jan 26, 2022 at 8:08 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Wednesday, February 2nd at* 11:30 am CT*
>
>
>
> *Where:*     Zoom Virtual Talk (*register in advance here*
> <https://uchicagogroup.zoom.us/webinar/register/WN_J_gBUwZZS5yH_x0S7eSTmw>
> )
>
>
> *Who: *       Ainesh Bakshi, Carnegie Mellon University
>
> *Title:*        Analytic Techniques for Robust Algorithm Design
>
> *Abstract:*
> Modern machine learning relies on algorithms that fit expressive models to
> large datasets. While such tasks are easy in low dimensions, real-world
> datasets are truly high-dimensional. Additionally, a prerequisite to
> deploying models in real-world systems is to ensure that their behavior
> degrades gracefully when the modeling assumptions no longer hold.
> Therefore, there is a growing need for *efficient algorithms* that fit
> reliable and robust models to data.
>
> In this talk, I will provide an overview of designing such efficient and
> robust algorithms, with provable guarantees, for fundamental tasks in
> machine learning and statistics. In particular, I will describe two
> complementary themes arising in this area: *high-dimensional robust
> statistics* and *fast numerical linear algebra*. The first addresses how
> to fit expressive models to high-dimensional datasets in the presence of
> outliers and the second develops fast algorithmic primitives to reduce
> dimensionality and de-noise large datasets. I will focus on recent results
> on robustly learning mixtures of arbitrary Gaussians and describe the new
> algorithmic ideas obtained along the way. Finally, I will make the case for
> analytic techniques, such as convex relaxations, being the natural choice
> for robust algorithm design.
>
> *Host:* *Yury Makarychev <yury at ttic.edu>*
>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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