[Theory] TODAY: 2/12 Talks at TTIC: Sidhanth Mohanty, MIT
Mary Marre via Theory
theory at mailman.cs.uchicago.edu
Wed Feb 12 09:56:12 CST 2025
*When:* Wednesday, February 12, 2025 at* 11:30** am** 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=b3cf547c-b2ed-49a8-a81c-b27c00337cb6>
*Who: * Sidhanth Mohanty, MIT
*Title:* A Quest for an Algorithmic Theory for High-Dimensional
Statistical Inference
*Abstract:* When does a statistical inference problem admit an efficient
algorithm?
There is an emergent body of research that studies this question by trying
to understand the power and limitations of various algorithmic paradigms in
solving statistical inference problems; for example, convex programming,
Markov chain Monte Carlo (MCMC) algorithms, and message passing algorithms
to name a few.
Of these, MCMC algorithms are easy to adapt to new inference problems and
have shown strong performance in practice, which makes them promising as a
universal algorithm for inference. However, provable guarantees for MCMC
have been scarce, lacking even for simple stylized models of inference.
In this talk, I will survey some recent strides that I have made with my
collaborators on achieving provable guarantees for MCMC in inference, and
some new tools we introduced for analyzing the behavior of slow-mixing
Markov chains.
*Bio: *Sidhanth is broadly interested in theoretical computer science and
probability theory, and his primary interests are on the algorithms and
complexity of statistical inference, and spectral graph theory.
Sidhanth is currently a postdoctoral researcher at MIT, hosted by Sam
Hopkins. Previously, he received his PhD in Computer Science at UC Berkeley
in 2023 where he was advised by Prasad Raghavendra.
*Host: **Madhur Tulsiani* <madhurt 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, Feb 11, 2025 at 1:25 PM Mary Marre <mmarre at ttic.edu> wrote:
> *When:* Wednesday, February 12, 2025 at* 11:30** am** 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=b3cf547c-b2ed-49a8-a81c-b27c00337cb6>
>
>
>
>
>
>
> *Who: * Sidhanth Mohanty, MIT
>
>
>
> *Title:* A Quest for an Algorithmic Theory for High-Dimensional
> Statistical Inference
>
> *Abstract:* When does a statistical inference problem admit an efficient
> algorithm?
>
> There is an emergent body of research that studies this question by trying
> to understand the power and limitations of various algorithmic paradigms in
> solving statistical inference problems; for example, convex programming,
> Markov chain Monte Carlo (MCMC) algorithms, and message passing algorithms
> to name a few.
>
> Of these, MCMC algorithms are easy to adapt to new inference problems and
> have shown strong performance in practice, which makes them promising as a
> universal algorithm for inference. However, provable guarantees for MCMC
> have been scarce, lacking even for simple stylized models of inference.
>
> In this talk, I will survey some recent strides that I have made with my
> collaborators on achieving provable guarantees for MCMC in inference, and
> some new tools we introduced for analyzing the behavior of slow-mixing
> Markov chains.
>
> *Bio: *Sidhanth is broadly interested in theoretical computer science and
> probability theory, and his primary interests are on the algorithms and
> complexity of statistical inference, and spectral graph theory.
>
> Sidhanth is currently a postdoctoral researcher at MIT, hosted by Sam
> Hopkins. Previously, he received his PhD in Computer Science at UC Berkeley
> in 2023 where he was advised by Prasad Raghavendra.
>
> *Host: **Madhur Tulsiani* <madhurt 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 Wed, Feb 5, 2025 at 9:41 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:* Wednesday, February 12, 2025 at* 11:30** am** 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=b3cf547c-b2ed-49a8-a81c-b27c00337cb6>
>>
>>
>>
>>
>>
>>
>> *Who: * Sidhanth Mohanty, MIT
>>
>>
>>
>> *Title:* A Quest for an Algorithmic Theory for High-Dimensional
>> Statistical Inference
>>
>> *Abstract:* When does a statistical inference problem admit an efficient
>> algorithm?
>>
>> There is an emergent body of research that studies this question by
>> trying to understand the power and limitations of various algorithmic
>> paradigms in solving statistical inference problems; for example, convex
>> programming, Markov chain Monte Carlo (MCMC) algorithms, and message
>> passing algorithms to name a few.
>>
>> Of these, MCMC algorithms are easy to adapt to new inference problems and
>> have shown strong performance in practice, which makes them promising as a
>> universal algorithm for inference. However, provable guarantees for MCMC
>> have been scarce, lacking even for simple stylized models of inference.
>>
>> In this talk, I will survey some recent strides that I have made with my
>> collaborators on achieving provable guarantees for MCMC in inference, and
>> some new tools we introduced for analyzing the behavior of slow-mixing
>> Markov chains.
>>
>> *Bio: *Sidhanth is broadly interested in theoretical computer science
>> and probability theory, and his primary interests are on the algorithms and
>> complexity of statistical inference, and spectral graph theory.
>>
>> Sidhanth is currently a postdoctoral researcher at MIT, hosted by Sam
>> Hopkins. Previously, he received his PhD in Computer Science at UC Berkeley
>> in 2023 where he was advised by Prasad Raghavendra.
>>
>> *Host: **Madhur Tulsiani* <madhurt 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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