[Theory] NOW: 1/30 Talks at TTIC: Benjamin Eysenbach, Carnegie Mellon
Mary Marre
mmarre at ttic.edu
Mon Jan 30 11:28:41 CST 2023
*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=a2093eb5-a01d-4901-8757-af94001ec02f>*
)
*Who: * Benjamin Eysenbach, Carnegie Mellon
------------------------------
*Title*: Self-Supervised Reinforcement Learning
*Abstract*: Reinforcement learning (RL) promises to harness the power of
machine learning to solve sequential decision making problems, with the
potential to enable applications ranging from robotics to chemistry.
However, what makes the RL paradigm broadly applicable is also what makes
it challenging: only limited feedback is provided for learning to select
good actions. In this talk, I will discuss how we have made headway of this
challenge by designing a class of self-supervised RL methods, ones that can
learn skills for acting using unsupervised (reward-free) experience. These
skill learning methods are practically-appealing and have since sparked a
vibrant area of research. I'll also share how we have answered some open
theoretical questions in this area.
*Bio*: Benjamin Eysenbach a final-year PhD student at Carnegie Mellon
University. His research has developed machine learning algorithms for
sequential decision making. His algorithms not only achieve a high degree
of performance, but also carry strong theoretical guarantees, are typically
simpler than prior methods, and draw connections between seemingly
disparate areas of ML and CS. Ben is the recipient of the NSF and Hertz
graduate fellowships. Prior to the PhD, he was a resident at Google
Research and studied math as an undergraduate at MIT.
*Host:* David McAllester <mcallester 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 30, 2023 at 10:16 AM 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=a2093eb5-a01d-4901-8757-af94001ec02f>*
> )
>
>
> *Who: * Benjamin Eysenbach, Carnegie Mellon
>
>
> ------------------------------
>
> *Title*: Self-Supervised Reinforcement Learning
>
> *Abstract*: Reinforcement learning (RL) promises to harness the power of
> machine learning to solve sequential decision making problems, with the
> potential to enable applications ranging from robotics to chemistry.
> However, what makes the RL paradigm broadly applicable is also what makes
> it challenging: only limited feedback is provided for learning to select
> good actions. In this talk, I will discuss how we have made headway of this
> challenge by designing a class of self-supervised RL methods, ones that can
> learn skills for acting using unsupervised (reward-free) experience. These
> skill learning methods are practically-appealing and have since sparked a
> vibrant area of research. I'll also share how we have answered some open
> theoretical questions in this area.
>
> *Bio*: Benjamin Eysenbach a final-year PhD student at Carnegie Mellon
> University. His research has developed machine learning algorithms for
> sequential decision making. His algorithms not only achieve a high degree
> of performance, but also carry strong theoretical guarantees, are typically
> simpler than prior methods, and draw connections between seemingly
> disparate areas of ML and CS. Ben is the recipient of the NSF and Hertz
> graduate fellowships. Prior to the PhD, he was a resident at Google
> Research and studied math as an undergraduate at MIT.
>
> *Host:* David McAllester <mcallester 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 Sun, Jan 29, 2023 at 2:49 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=a2093eb5-a01d-4901-8757-af94001ec02f>*
>> )
>>
>>
>> *Who: * Benjamin Eysenbach, Carnegie Mellon
>>
>>
>> ------------------------------
>>
>> *Title*: Self-Supervised Reinforcement Learning
>>
>> *Abstract*: Reinforcement learning (RL) promises to harness the power of
>> machine learning to solve sequential decision making problems, with the
>> potential to enable applications ranging from robotics to chemistry.
>> However, what makes the RL paradigm broadly applicable is also what makes
>> it challenging: only limited feedback is provided for learning to select
>> good actions. In this talk, I will discuss how we have made headway of this
>> challenge by designing a class of self-supervised RL methods, ones that can
>> learn skills for acting using unsupervised (reward-free) experience. These
>> skill learning methods are practically-appealing and have since sparked a
>> vibrant area of research. I'll also share how we have answered some open
>> theoretical questions in this area.
>>
>> *Bio*: Benjamin Eysenbach a final-year PhD student at Carnegie Mellon
>> University. His research has developed machine learning algorithms for
>> sequential decision making. His algorithms not only achieve a high degree
>> of performance, but also carry strong theoretical guarantees, are typically
>> simpler than prior methods, and draw connections between seemingly
>> disparate areas of ML and CS. Ben is the recipient of the NSF and Hertz
>> graduate fellowships. Prior to the PhD, he was a resident at Google
>> Research and studied math as an undergraduate at MIT.
>>
>> *Host:* David McAllester <mcallester 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 23, 2023 at 7:58 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=a2093eb5-a01d-4901-8757-af94001ec02f>*
>>> )
>>>
>>>
>>> *Who: * Benjamin Eysenbach, Carnegie Mellon
>>>
>>>
>>> ------------------------------
>>>
>>> *Title*: Self-Supervised Reinforcement Learning
>>>
>>> *Abstract*: Reinforcement learning (RL) promises to harness the power
>>> of machine learning to solve sequential decision making problems, with the
>>> potential to enable applications ranging from robotics to chemistry.
>>> However, what makes the RL paradigm broadly applicable is also what makes
>>> it challenging: only limited feedback is provided for learning to select
>>> good actions. In this talk, I will discuss how we have made headway of this
>>> challenge by designing a class of self-supervised RL methods, ones that can
>>> learn skills for acting using unsupervised (reward-free) experience. These
>>> skill learning methods are practically-appealing and have since sparked a
>>> vibrant area of research. I'll also share how we have answered some open
>>> theoretical questions in this area.
>>>
>>> *Bio*: Benjamin Eysenbach a final-year PhD student at Carnegie Mellon
>>> University. His research has developed machine learning algorithms for
>>> sequential decision making. His algorithms not only achieve a high degree
>>> of performance, but also carry strong theoretical guarantees, are typically
>>> simpler than prior methods, and draw connections between seemingly
>>> disparate areas of ML and CS. Ben is the recipient of the NSF and Hertz
>>> graduate fellowships. Prior to the PhD, he was a resident at Google
>>> Research and studied math as an undergraduate at MIT.
>>>
>>> *Host:* David McAllester <mcallester 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>*
>>>
>>
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