[Theory] REMINDER: 1/23 Talks at TTIC: Victor Zhong, University of Washington

[Mary Marre]

*When:*        Monday, *January 23rd* at* 11:30** a**m CT   *


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Virtually:*  *via* Panopto (*livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=69dd2edf-2845-4f0d-bb18-af8d0015033c>*
)


*Who: *         Victor Zhong, University of Washington


------------------------------

*Title:           *Reading to Learn: Improving Generalization by Learning
>From Language

*Abstract: *Traditional machine learning (ML) systems are trained on vast
quantities of annotated data or experience. These systems often do not
generalize to new, related problems that emerge after training, such as
conversing about new topics or interacting with new environments. In this
talk, I present Reading to Learn, a new class of algorithms that improve
generalization by learning to read language specifications, without
requiring any actual experience or labeled examples. This includes, for
example, reading FAQ documents to learn to answer new questions and reading
manuals to learn to play new games. I will discuss new algorithms and data
for Reading to Learn applied to a broad range of tasks, including
pretraining for grounded reinforcement learning, data synthesis for code
generation, and task-oriented dialogue about new topics, while also
highlighting open challenges for this line of work.  Ultimately, the goal
of Reading to Learn is to democratize AI by making it accessible for
low-resource problems where the practitioner cannot obtain annotated data
at scale, but can instead write language specifications that models read to
generalize.

*Bio:* Victor Zhong is a PhD student at the University of Washington
Natural Language Processing group. His research is at the intersection of
natural language processing and machine learning, with an emphasis on how
to use language understanding to learn more generally and more efficiently.
His research covers a range of topics, including dialogue, code generation,
question answering, and grounded reinforcement learning. Victor has been
awarded the Apple AI/ML Fellowship as well as an EMNLP Outstanding Paper
award. His work has been featured in Wired, MIT Technology Review,
TechCrunch, VentureBeat, Fast Company, and Quanta Magazine. He was a
founding member of Salesforce Research, and has previously worked at Meta
AI Research and Google Brain. He obtained a Masters in Computer Science
from Stanford University and a Bachelor of Applied Science in Computer
Engineering from the University of Toronto.
*Host:* Karen Livescu <klivescu at ttic.edu>



Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL  60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Tue, Jan 17, 2023 at 8:00 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Monday, January 23rd at* 11:30** a**m CT   *
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
> *Virtually:*  *via* Panopto (*livestream
> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=69dd2edf-2845-4f0d-bb18-af8d0015033c>*
> )
>
>
> *Who: *         Victor Zhong, University of Washington
>
>
> ------------------------------
>
> *Title:           *Reading to Learn: Improving Generalization by Learning
> From Language
>
> *Abstract: *Traditional machine learning (ML) systems are trained on vast
> quantities of annotated data or experience. These systems often do not
> generalize to new, related problems that emerge after training, such as
> conversing about new topics or interacting with new environments. In this
> talk, I present Reading to Learn, a new class of algorithms that improve
> generalization by learning to read language specifications, without
> requiring any actual experience or labeled examples. This includes, for
> example, reading FAQ documents to learn to answer new questions and reading
> manuals to learn to play new games. I will discuss new algorithms and data
> for Reading to Learn applied to a broad range of tasks, including
> pretraining for grounded reinforcement learning, data synthesis for code
> generation, and task-oriented dialogue about new topics, while also
> highlighting open challenges for this line of work.  Ultimately, the goal
> of Reading to Learn is to democratize AI by making it accessible for
> low-resource problems where the practitioner cannot obtain annotated data
> at scale, but can instead write language specifications that models read to
> generalize.
>
> *Bio:* Victor Zhong is a PhD student at the University of Washington
> Natural Language Processing group. His research is at the intersection of
> natural language processing and machine learning, with an emphasis on how
> to use language understanding to learn more generally and more efficiently.
> His research covers a range of topics, including dialogue, code generation,
> question answering, and grounded reinforcement learning. Victor has been
> awarded the Apple AI/ML Fellowship as well as an EMNLP Outstanding Paper
> award. His work has been featured in Wired, MIT Technology Review,
> TechCrunch, VentureBeat, Fast Company, and Quanta Magazine. He was a
> founding member of Salesforce Research, and has previously worked at Meta
> AI Research and Google Brain. He obtained a Masters in Computer Science
> from Stanford University and a Bachelor of Applied Science in Computer
> Engineering from the University of Toronto.
> *Host:* Karen Livescu <klivescu at ttic.edu>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue, Rm 517*
> *Chicago, IL  60637*
> *773-834-1757*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Mon, Jan 16, 2023 at 7:37 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*        Monday, January 30th at* 11:30** a**m CT   *
>>
>>
>> *Where:       *Talk will be given *live, in-person* at
>>
>>                    TTIC, 6045 S. Kenwood Avenue
>>
>>                    5th Floor, Room 530
>>
>>
>> *Virtually:*  *via* Panopto (*livestream
>> <https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=69dd2edf-2845-4f0d-bb18-af8d0015033c>*
>> )
>>
>>
>> *Who: *         Victor Zhong, University of Washington
>>
>>
>> ------------------------------
>>
>> *Title:           *Reading to Learn: Improving Generalization by
>> Learning From Language
>>
>> *Abstract: *Traditional machine learning (ML) systems are trained on
>> vast quantities of annotated data or experience. These systems often do not
>> generalize to new, related problems that emerge after training, such as
>> conversing about new topics or interacting with new environments. In this
>> talk, I present Reading to Learn, a new class of algorithms that improve
>> generalization by learning to read language specifications, without
>> requiring any actual experience or labeled examples. This includes, for
>> example, reading FAQ documents to learn to answer new questions and reading
>> manuals to learn to play new games. I will discuss new algorithms and data
>> for Reading to Learn applied to a broad range of tasks, including
>> pretraining for grounded reinforcement learning, data synthesis for code
>> generation, and task-oriented dialogue about new topics, while also
>> highlighting open challenges for this line of work.  Ultimately, the goal
>> of Reading to Learn is to democratize AI by making it accessible for
>> low-resource problems where the practitioner cannot obtain annotated data
>> at scale, but can instead write language specifications that models read to
>> generalize.
>>
>> *Bio:* Victor Zhong is a PhD student at the University of Washington
>> Natural Language Processing group. His research is at the intersection of
>> natural language processing and machine learning, with an emphasis on how
>> to use language understanding to learn more generally and more efficiently.
>> His research covers a range of topics, including dialogue, code generation,
>> question answering, and grounded reinforcement learning. Victor has been
>> awarded the Apple AI/ML Fellowship as well as an EMNLP Outstanding Paper
>> award. His work has been featured in Wired, MIT Technology Review,
>> TechCrunch, VentureBeat, Fast Company, and Quanta Magazine. He was a
>> founding member of Salesforce Research, and has previously worked at Meta
>> AI Research and Google Brain. He obtained a Masters in Computer Science
>> from Stanford University and a Bachelor of Applied Science in Computer
>> Engineering from the University of Toronto.
>> *Host:* Karen Livescu <klivescu at ttic.edu>
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue, Rm 517*
>> *Chicago, IL  60637*
>> *773-834-1757*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/theory/attachments/20230122/7b4ea6bb/attachment-0001.html>