[Theory] REMINDER: 6/24 TTIC Colloquium: Lav R. Varshney, University of Illinois at Urbana-Champaign

[Mary Marre]

*TTIC Colloquium*

[image: image.png]

*When:*      Monday, June 24th at 11:00 am



*Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526



*Who: *       Lav R. Varshney, University of Illinois at Urbana-Champaign



*Title: *       Human-Interpretable Concept Learning via Information
Lattices

*Abstract:* Is it possible to learn the laws of music theory directly from
raw sheet music in the same human-interpretable form as a music theory
textbook?  How little prior knowledge needs to be encoded to do so?  We
consider these and similar questions in other topical domains, in
developing a general framework for automatic concept learning.  The basic
idea is an iterative discovery algorithm that has a student-teacher
architecture and that operates on a generalization of Shannon’s information
lattice, which itself encodes a hierarchy of abstractions and is
algorithmically constructed from group-theoretic foundations.  In
particular, learning this hierarchy of invariant concepts involves
iterative optimization of Bayesian surprise and entropy.  This gives a
first step towards a principled and cognitive way of automatic concept
learning and knowledge discovery.  We further discuss applications in
computational creativity, AI safety, and AI ethics.

 *Biography: *Lav Varshney is an assistant professor of electrical and
computer engineering, computer science, and neuroscience at the University
of Illinois at Urbana-Champaign.  He is also chief scientist of Ensaras,
Inc.  He received the B.S. degree (magna cum laude) with honors from
Cornell University in 2004. He received the S.M., E.E., and Ph.D. degrees
from the Massachusetts Institute of Technology in 2006, 2008, and 2010,
where his theses received the E. A. Guillemin Thesis Award and the J.-A.
Kong Award Honorable Mention. He was a research staff member at the IBM
Thomas J. Watson Research Center from 2010 until 2013, where he led the
design and development of the Chef Watson computational creativity system.
His research interests include information and coding theory; data science
and artificial intelligence; and limits of nanoscale, social, and neural
computing.

Dr. Varshney serves on the advisory board of the AI XPRIZE.  He received
the IBM Faculty Award in 2014 and was a finalist for the Bell Labs Prize in
2014 and 2016. He and his students have won several best paper awards, his
work appears in the anthology, The Best Writing on Mathematics 2014, and he
was selected to present at the 2017 World Science Festival.  He appears on
the List of Teachers Ranked as Excellent and has been named a Center for
Advanced Study Fellow at the University of Illinois.


Host: Mesrob Ohannessian <mesrob at ttic.edu>





Mary C. Marre
Administrative Assistant
*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 Mon, Jun 17, 2019 at 7:18 PM Mary Marre <mmarre at ttic.edu> wrote:

> *TTIC Colloquium*
>
> [image: image.png]
>
> *When:*      Monday, June 24th at 11:00 am
>
>
>
> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who: *       Lav R. Varshney, University of Illinois at Urbana-Champaign
>
>
>
> *Title: *       Human-Interpretable Concept Learning via Information
> Lattices
>
> *Abstract:* Is it possible to learn the laws of music theory directly
> from raw sheet music in the same human-interpretable form as a music theory
> textbook?  How little prior knowledge needs to be encoded to do so?  We
> consider these and similar questions in other topical domains, in
> developing a general framework for automatic concept learning.  The basic
> idea is an iterative discovery algorithm that has a student-teacher
> architecture and that operates on a generalization of Shannon’s information
> lattice, which itself encodes a hierarchy of abstractions and is
> algorithmically constructed from group-theoretic foundations.  In
> particular, learning this hierarchy of invariant concepts involves
> iterative optimization of Bayesian surprise and entropy.  This gives a
> first step towards a principled and cognitive way of automatic concept
> learning and knowledge discovery.  We further discuss applications in
> computational creativity, AI safety, and AI ethics.
>
>  *Biography: *Lav Varshney is an assistant professor of electrical and
> computer engineering, computer science, and neuroscience at the University
> of Illinois at Urbana-Champaign.  He is also chief scientist of Ensaras,
> Inc.  He received the B.S. degree (magna cum laude) with honors from
> Cornell University in 2004. He received the S.M., E.E., and Ph.D. degrees
> from the Massachusetts Institute of Technology in 2006, 2008, and 2010,
> where his theses received the E. A. Guillemin Thesis Award and the J.-A.
> Kong Award Honorable Mention. He was a research staff member at the IBM
> Thomas J. Watson Research Center from 2010 until 2013, where he led the
> design and development of the Chef Watson computational creativity system.
> His research interests include information and coding theory; data science
> and artificial intelligence; and limits of nanoscale, social, and neural
> computing.
>
> Dr. Varshney serves on the advisory board of the AI XPRIZE.  He received
> the IBM Faculty Award in 2014 and was a finalist for the Bell Labs Prize in
> 2014 and 2016. He and his students have won several best paper awards, his
> work appears in the anthology, The Best Writing on Mathematics 2014, and he
> was selected to present at the 2017 World Science Festival.  He appears on
> the List of Teachers Ranked as Excellent and has been named a Center for
> Advanced Study Fellow at the University of Illinois.
>
>
> Host: Mesrob Ohannessian <mesrob at ttic.edu>
>
>
> Mary C. Marre
> Administrative Assistant
> *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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