[Colloquium] NOW: 3/10 Talks at TTIC: Aditi Raghunathan, Stanford University

[Mary Marre]

*When:*      Wednesday, March 10th at* 11:10 am CT*



*Where:*     Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_sQlwfNDJRB2lp8s1ARxV9w>*)



*Who: *       Aditi Raghunathan, Stanford University



*Title:* Rethinking the Role of Data in Robust Machine Learning

*Abstract:* Despite notable successes on several carefully controlled
benchmarks, current machine learning (ML) systems are remarkably brittle,
raising serious concerns about their deployment in safety-critical
applications like self-driving cars and predictive healthcare. In this
talk, I discuss fundamental obstacles to building robust ML systems and
develop principled approaches that form the foundations of robust ML. In
particular, I will focus on the role of data and demonstrate the need to
question common assumptions when improving robustness to (i) adversarial
examples and (ii) spurious correlations. On the one hand, I will describe
how and why naively using more data can surprisingly hurt performance in
these settings. On the other hand, I will show that unlabeled data, when
harnessed in the right fashion, is extremely beneficial and achieves
state-of-the-art robustness. In closing, I will discuss how to build on the
foundations of robust ML and achieve wide-ranging robustness in various
domains including natural language processing and vision.

*Bio:* Aditi Raghunathan is a fifth year PhD student at Stanford University
advised by Percy Liang. She is interested in building robust machine
learning systems with guarantees for trustworthy real-world deployment. Her
research in robustness has been recognized by a Google PhD Fellowship in
Machine Learning and the Open Philanthropy AI Fellowship. Among other
honors, she is also the recipient of the Anita Borg Memorial Scholarship
and the Stanford School of Engineering Fellowship.



*Host:* *David McAllester* <mcallester at ttic.edu>



Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Wed, Mar 10, 2021 at 10:10 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Wednesday, March 10th at* 11:10 am CT*
>
>
>
> *Where:*     Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_sQlwfNDJRB2lp8s1ARxV9w>*
> )
>
>
>
> *Who: *       Aditi Raghunathan, Stanford University
>
>
>
> *Title:* Rethinking the Role of Data in Robust Machine Learning
>
> *Abstract:* Despite notable successes on several carefully controlled
> benchmarks, current machine learning (ML) systems are remarkably brittle,
> raising serious concerns about their deployment in safety-critical
> applications like self-driving cars and predictive healthcare. In this
> talk, I discuss fundamental obstacles to building robust ML systems and
> develop principled approaches that form the foundations of robust ML. In
> particular, I will focus on the role of data and demonstrate the need to
> question common assumptions when improving robustness to (i) adversarial
> examples and (ii) spurious correlations. On the one hand, I will describe
> how and why naively using more data can surprisingly hurt performance in
> these settings. On the other hand, I will show that unlabeled data, when
> harnessed in the right fashion, is extremely beneficial and achieves
> state-of-the-art robustness. In closing, I will discuss how to build on the
> foundations of robust ML and achieve wide-ranging robustness in various
> domains including natural language processing and vision.
>
> *Bio:* Aditi Raghunathan is a fifth year PhD student at Stanford
> University advised by Percy Liang. She is interested in building robust
> machine learning systems with guarantees for trustworthy real-world
> deployment. Her research in robustness has been recognized by a Google PhD
> Fellowship in Machine Learning and the Open Philanthropy AI Fellowship.
> Among other honors, she is also the recipient of the Anita Borg Memorial
> Scholarship and the Stanford School of Engineering Fellowship.
>
>
>
> *Host:* *David McAllester* <mcallester at ttic.edu>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Tue, Mar 9, 2021 at 3:48 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*      Wednesday, March 10th at* 11:10 am CT*
>>
>>
>>
>> *Where:*     Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_sQlwfNDJRB2lp8s1ARxV9w>*
>> )
>>
>>
>>
>> *Who: *       Aditi Raghunathan, Stanford University
>>
>>
>>
>> *Title:* Rethinking the Role of Data in Robust Machine Learning
>>
>> *Abstract:* Despite notable successes on several carefully controlled
>> benchmarks, current machine learning (ML) systems are remarkably brittle,
>> raising serious concerns about their deployment in safety-critical
>> applications like self-driving cars and predictive healthcare. In this
>> talk, I discuss fundamental obstacles to building robust ML systems and
>> develop principled approaches that form the foundations of robust ML. In
>> particular, I will focus on the role of data and demonstrate the need to
>> question common assumptions when improving robustness to (i) adversarial
>> examples and (ii) spurious correlations. On the one hand, I will describe
>> how and why naively using more data can surprisingly hurt performance in
>> these settings. On the other hand, I will show that unlabeled data, when
>> harnessed in the right fashion, is extremely beneficial and achieves
>> state-of-the-art robustness. In closing, I will discuss how to build on the
>> foundations of robust ML and achieve wide-ranging robustness in various
>> domains including natural language processing and vision.
>>
>> *Bio:* Aditi Raghunathan is a fifth year PhD student at Stanford
>> University advised by Percy Liang. She is interested in building robust
>> machine learning systems with guarantees for trustworthy real-world
>> deployment. Her research in robustness has been recognized by a Google PhD
>> Fellowship in Machine Learning and the Open Philanthropy AI Fellowship.
>> Among other honors, she is also the recipient of the Anita Borg Memorial
>> Scholarship and the Stanford School of Engineering Fellowship.
>>
>>
>>
>> *Host:* *David McAllester* <mcallester at ttic.edu>
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 517*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757*
>> *f: (773) 357-6970*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>>
>> On Thu, Mar 4, 2021 at 1:40 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:*      Wednesday, March 10th at* 11:10 am CT*
>>>
>>>
>>>
>>> *Where:*     Zoom Virtual Talk (*register in advance here
>>> <https://uchicagogroup.zoom.us/webinar/register/WN_sQlwfNDJRB2lp8s1ARxV9w>*
>>> )
>>>
>>>
>>>
>>> *Who: *       Aditi Raghunathan, Stanford University
>>>
>>>
>>>
>>> *Title:* Rethinking the Role of Data in Robust Machine Learning
>>>
>>> *Abstract:* Despite notable successes on several carefully controlled
>>> benchmarks, current machine learning (ML) systems are remarkably brittle,
>>> raising serious concerns about their deployment in safety-critical
>>> applications like self-driving cars and predictive healthcare. In this
>>> talk, I discuss fundamental obstacles to building robust ML systems and
>>> develop principled approaches that form the foundations of robust ML. In
>>> particular, I will focus on the role of data and demonstrate the need to
>>> question common assumptions when improving robustness to (i) adversarial
>>> examples and (ii) spurious correlations. On the one hand, I will describe
>>> how and why naively using more data can surprisingly hurt performance in
>>> these settings. On the other hand, I will show that unlabeled data, when
>>> harnessed in the right fashion, is extremely beneficial and achieves
>>> state-of-the-art robustness. In closing, I will discuss how to build on the
>>> foundations of robust ML and achieve wide-ranging robustness in various
>>> domains including natural language processing and vision.
>>>
>>> *Bio:* Aditi Raghunathan is a fifth year PhD student at Stanford
>>> University advised by Percy Liang. She is interested in building robust
>>> machine learning systems with guarantees for trustworthy real-world
>>> deployment. Her research in robustness has been recognized by a Google PhD
>>> Fellowship in Machine Learning and the Open Philanthropy AI Fellowship.
>>> Among other honors, she is also the recipient of the Anita Borg Memorial
>>> Scholarship and the Stanford School of Engineering Fellowship.
>>>
>>>
>>>
>>> *Host:* *David McAllester* <mcallester at ttic.edu>
>>>
>>>
>>>
>>>
>>>
>>> Mary C. Marre
>>> Faculty Administrative Support
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue*
>>> *Room 517*
>>> *Chicago, IL  60637*
>>> *p:(773) 834-1757*
>>> *f: (773) 357-6970*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>
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