[Theory] REMINDER: 2/4 Talks at TTIC: Luiz Chamon, University of Pennsylvania

[Mary Marre]

*When:*      Thursday, February 4th at* 11:10 am CT*



*Where:*     Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_lfvDnWXVT6a7KXFitSzpZA>*)



*Who: *       Luiz Chamon, University of Pennsylvania


*Title:*         Learning under Requirements

*Abstract:* The transformative power of learning lies in automating the
design of complex systems, allowing us to go from data to operation with
little to no human intervention. Today, however, learning today does not
incorporate requirements organically, which has led to data-driven
solutions prone to tampering, unsafe behavior, and biased, prejudiced
actions. To realize its autonomous engineering potential, we must develop
learning methods capable of satisfying requirements beyond the training
data. In this talk, I will show when and how it is possible to learn under
requirements by developing the theoretical underpinnings of constrained
learning. I will define constrained learning by extending the classical
probably approximately correct (PAC) framework and show that despite
appearances, constrained learning is not harder than unconstrained
learning. In fact, they have essentially the same sample complexity. I will
also derive a practical learning rule that under mild conditions can tackle
constrained learning tasks by solving only unconstrained empirical risk
minimization (ERM) problems, a duality that holds despite the lack of
convexity. I will illustrate how these advances enable the data-driven
design of trustworthy systems that adhere to fairness, robustness, and
safety specifications. I see these contributions as advancing beyond the
current objective-centric learning paradigm towards a constraint-driven
learning one, that I will briefly discuss together with the new theoretical
and practical questions it raises.

*Bio:* Luiz Chamon received the B.Sc. and M.Sc. degrees in electrical
engineering from the University of São Paulo, São Paulo, Brazil, in 2011
and 2015 and the Ph.D. degree in electrical and systems engineering from
the University of Pennsylvania (Penn), Philadelphia, in 2020. He is
currently a postdoc of the Department of Electrical and Systems Engineering
of the University of Pennsylvania. In 2009, he was an undergraduate
exchange student of the Masters in Acoustics of the École Centrale de Lyon,
Lyon, France, and worked as an Assistant Instructor and Consultant on
nondestructive testing at INSACAST Formation Continue. From 2010 to 2014,
he worked as a Signal Processing and Statistics Consultant on a research
project with EMBRAER. In 2018, he was recognized by the IEEE Signal
Processing Society for his distinguished work for the editorial board of
the IEEE Transactions on Signal Processing. He also received both the best
student paper and the best paper awards at IEEE ICASSP 2020. His research
interests include optimization, signal processing, machine learning,
statistics, and control.

*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, Feb 3, 2021 at 3:12 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Thursday, February 4th at* 11:10 am CT*
>
>
>
> *Where:*     Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_lfvDnWXVT6a7KXFitSzpZA>*
> )
>
>
>
> *Who: *       Luiz Chamon, University of Pennsylvania
>
>
> *Title:*         Learning under Requirements
>
> *Abstract:* The transformative power of learning lies in automating the
> design of complex systems, allowing us to go from data to operation with
> little to no human intervention. Today, however, learning today does not
> incorporate requirements organically, which has led to data-driven
> solutions prone to tampering, unsafe behavior, and biased, prejudiced
> actions. To realize its autonomous engineering potential, we must develop
> learning methods capable of satisfying requirements beyond the training
> data. In this talk, I will show when and how it is possible to learn under
> requirements by developing the theoretical underpinnings of constrained
> learning. I will define constrained learning by extending the classical
> probably approximately correct (PAC) framework and show that despite
> appearances, constrained learning is not harder than unconstrained
> learning. In fact, they have essentially the same sample complexity. I will
> also derive a practical learning rule that under mild conditions can tackle
> constrained learning tasks by solving only unconstrained empirical risk
> minimization (ERM) problems, a duality that holds despite the lack of
> convexity. I will illustrate how these advances enable the data-driven
> design of trustworthy systems that adhere to fairness, robustness, and
> safety specifications. I see these contributions as advancing beyond the
> current objective-centric learning paradigm towards a constraint-driven
> learning one, that I will briefly discuss together with the new theoretical
> and practical questions it raises.
>
> *Bio:* Luiz Chamon received the B.Sc. and M.Sc. degrees in electrical
> engineering from the University of São Paulo, São Paulo, Brazil, in 2011
> and 2015 and the Ph.D. degree in electrical and systems engineering from
> the University of Pennsylvania (Penn), Philadelphia, in 2020. He is
> currently a postdoc of the Department of Electrical and Systems Engineering
> of the University of Pennsylvania. In 2009, he was an undergraduate
> exchange student of the Masters in Acoustics of the École Centrale de Lyon,
> Lyon, France, and worked as an Assistant Instructor and Consultant on
> nondestructive testing at INSACAST Formation Continue. From 2010 to 2014,
> he worked as a Signal Processing and Statistics Consultant on a research
> project with EMBRAER. In 2018, he was recognized by the IEEE Signal
> Processing Society for his distinguished work for the editorial board of
> the IEEE Transactions on Signal Processing. He also received both the best
> student paper and the best paper awards at IEEE ICASSP 2020. His research
> interests include optimization, signal processing, machine learning,
> statistics, and control.
>
> *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, Jan 28, 2021 at 4:48 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*      Thursday, February 4th at* 11:10 am CT*
>>
>>
>>
>> *Where:*     Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_lfvDnWXVT6a7KXFitSzpZA>*
>> )
>>
>>
>>
>> *Who: *       Luiz Chamon, University of Pennsylvania
>>
>>
>> *Title:*         Learning under Requirements
>>
>> *Abstract:* The transformative power of learning lies in automating the
>> design of complex systems, allowing us to go from data to operation with
>> little to no human intervention. Today, however, learning today does not
>> incorporate requirements organically, which has led to data-driven
>> solutions prone to tampering, unsafe behavior, and biased, prejudiced
>> actions. To realize its autonomous engineering potential, we must develop
>> learning methods capable of satisfying requirements beyond the training
>> data. In this talk, I will show when and how it is possible to learn under
>> requirements by developing the theoretical underpinnings of constrained
>> learning. I will define constrained learning by extending the classical
>> probably approximately correct (PAC) framework and show that despite
>> appearances, constrained learning is not harder than unconstrained
>> learning. In fact, they have essentially the same sample complexity. I will
>> also derive a practical learning rule that under mild conditions can tackle
>> constrained learning tasks by solving only unconstrained empirical risk
>> minimization (ERM) problems, a duality that holds despite the lack of
>> convexity. I will illustrate how these advances enable the data-driven
>> design of trustworthy systems that adhere to fairness, robustness, and
>> safety specifications. I see these contributions as advancing beyond the
>> current objective-centric learning paradigm towards a constraint-driven
>> learning one, that I will briefly discuss together with the new theoretical
>> and practical questions it raises.
>>
>> *Bio:* Luiz Chamon received the B.Sc. and M.Sc. degrees in electrical
>> engineering from the University of São Paulo, São Paulo, Brazil, in 2011
>> and 2015 and the Ph.D. degree in electrical and systems engineering from
>> the University of Pennsylvania (Penn), Philadelphia, in 2020. He is
>> currently a postdoc of the Department of Electrical and Systems Engineering
>> of the University of Pennsylvania. In 2009, he was an undergraduate
>> exchange student of the Masters in Acoustics of the École Centrale de Lyon,
>> Lyon, France, and worked as an Assistant Instructor and Consultant on
>> nondestructive testing at INSACAST Formation Continue. From 2010 to 2014,
>> he worked as a Signal Processing and Statistics Consultant on a research
>> project with EMBRAER. In 2018, he was recognized by the IEEE Signal
>> Processing Society for his distinguished work for the editorial board of
>> the IEEE Transactions on Signal Processing. He also received both the best
>> student paper and the best paper awards at IEEE ICASSP 2020. His research
>> interests include optimization, signal processing, machine learning,
>> statistics, and control.
>>
>> *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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