Oct 26 at 12pm (Zoom) -- Chen Dan (CMU CSD) -- Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification -- AI Seminar sponsored by Morgan Stanley

[Shaojie Bai]

Hi all,

The seminar today by Chen Dan on adversarial robustness is happening right
now!

In case you are interested:
https://cmu.zoom.us/j/93155268338?pwd=VVZYTFFEMTNLZlJVY1NmU1c3cXUzZz09

Best,
Shaojie

On Mon, Oct 25, 2021 at 8:18 PM Shaojie Bai <shaojieb at cs.cmu.edu> wrote:

> Hi all,
>
> Just a reminder that the CMU AI Seminar is tomorrow 12pm-1pm:
> https://cmu.zoom.us/j/93155268338?pwd=VVZYTFFEMTNLZlJVY1NmU1c3cXUzZz09   .
>
> Chen Dan (CMU CSD) will be talking about his recent work on statistical
> understanding of adversarial robustness.
>
> Thanks,
> Shaojie
>
> On Sat, Oct 23, 2021 at 3:08 PM Shaojie Bai <shaojieb at cs.cmu.edu> wrote:
>
>> Dear all,
>>
>> We look forward to seeing you *next Tuesday (10/26)* from *1**2:00-1:00
>> PM (U.S. Eastern time)* for the next talk of our *CMU AI seminar*,
>> sponsored by Morgan Stanley
>> <https://www.morganstanley.com/about-us/technology/>.
>>
>> To learn more about the seminar series or see the future schedule, please
>> visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.
>>
>> On 10/26, *Chen Dan* (CMU CSD) will be giving a talk on "*Sharp
>> Statistical Guarantees for Adversarially Robust Gaussian Classification*
>> ".
>>
>> *Title*: Sharp Statistical Guarantees for Adversarially Robust Gaussian
>> Classification
>>
>> *Talk Abstract*: Adversarial robustness has become a fundamental
>> requirement in modern machine learning applications. Yet, there has been
>> surprisingly little statistical understanding so far. In this work, we
>> provide the first result of the optimal minimax guarantees for the excess
>> risk for adversarially robust classification, under a Gaussian mixture
>> model studied by Schmidt et al. 2018. The results are stated in terms of
>> the Adversarial Signal-to-Noise Ratio (AdvSNR), which generalizes a similar
>> notion for standard linear classification to the adversarial setting. We
>> establish an excess risk lower bound and design a computationally efficient
>> estimator that achieves this optimal rate. Our results built upon a minimal
>> set of assumptions while covering a wide spectrum of adversarial
>> perturbations including L_p balls for any p>1. Joint work with Yuting Wei
>> and Pradeep Ravikumar.
>>
>> *Speaker Bio*:  Chen Dan is a 6th year Ph.D. student at Computer Science
>> Department, Carnegie Mellon University, advised by Pradeep Ravikumar. His
>> research interest is in the broad area of robust statistical learning, with
>> an emphasis on the theoretical understanding and practical algorithms for
>> learning under various types of adversarial distribution shift. Prior to
>> joining CMU, Chen received his bachelor degree from School of EECS, Peking
>> University in 2016.
>>
>> *Zoom Link*:
>> https://cmu.zoom.us/j/93155268338?pwd=VVZYTFFEMTNLZlJVY1NmU1c3cXUzZz09
>>
>> Thanks,
>> Shaojie Bai (MLD)
>>
>
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