[Theory] NOW: 11/14 TTIC Colloquium: Paul Valiant, Purdue University
Mary Marre
mmarre at ttic.edu
Mon Nov 14 23:28:00 CST 2022
*When:* Monday, November 14th at* 11:30 am CT*
*Where: * Talk will be *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=a6540580-fb4e-4402-b1a9-af460182dc1f>*
)
*Who: * Paul Valiant, Purdue University
------------------------------
*Title:* Mean Estimation in Low and High Dimensions
*Abstract: *This talk will discuss the fundamental statistical problem of
estimating the mean of a distribution, as accurately as possible given
samples from it. This problem arises both as a subcomponent of many
algorithms, and also in practice as one of the most important data
primitives when dealing with real-world data. While many variants and
extensions of this problem have been proposed and analyzed, in this talk I
will discuss two of the most iconic: 1) when the data comes from a
real-valued distribution, and 2) when the data comes from a
high-dimensional vector-valued distribution. In both cases, we achieve the
first estimators whose accuracy is optimal to 1+o(1) factors, optimal in
its dependence on the unknown (co-) variance of the underlying
distribution, the number of samples n, and the desired confidence delta. I
will highlight some of the crucial and novel analytical tools used in the
analysis, and in particular, draw attention to a new "vector Bernstein
inequality" which makes precise the intuition that sums of bounded
independent random variables in increasingly high dimensions increasingly
"adhere to a spherical shell". These results suggest several possible
extensions in this large and active area of statistical estimation
research. This talk is based on joint work with Jasper C.H. Lee.
Host: *Madhur Tulsiani* <madhurt at ttic.edu>
For more information on the *C**olloquium** Series* or to subscribe to the
mailing list, please see http://www.ttic.edu/colloquium.php
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, Nov 14, 2022 at 10:30 AM Mary Marre <mmarre at ttic.edu> wrote:
> *When:* Monday, November 14th at* 11:30 am CT*
>
> *Where: * Talk will be *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=a6540580-fb4e-4402-b1a9-af460182dc1f>*
> )
>
>
> *Who: * Paul Valiant, Purdue University
>
>
> ------------------------------
>
> *Title:* Mean Estimation in Low and High Dimensions
> *Abstract: *This talk will discuss the fundamental statistical problem of
> estimating the mean of a distribution, as accurately as possible given
> samples from it. This problem arises both as a subcomponent of many
> algorithms, and also in practice as one of the most important data
> primitives when dealing with real-world data. While many variants and
> extensions of this problem have been proposed and analyzed, in this talk I
> will discuss two of the most iconic: 1) when the data comes from a
> real-valued distribution, and 2) when the data comes from a
> high-dimensional vector-valued distribution. In both cases, we achieve the
> first estimators whose accuracy is optimal to 1+o(1) factors, optimal in
> its dependence on the unknown (co-) variance of the underlying
> distribution, the number of samples n, and the desired confidence delta. I
> will highlight some of the crucial and novel analytical tools used in the
> analysis, and in particular, draw attention to a new "vector Bernstein
> inequality" which makes precise the intuition that sums of bounded
> independent random variables in increasingly high dimensions increasingly
> "adhere to a spherical shell". These results suggest several possible
> extensions in this large and active area of statistical estimation
> research. This talk is based on joint work with Jasper C.H. Lee.
>
> Host: *Madhur Tulsiani* <madhurt at ttic.edu>
>
> For more information on the *C**olloquium** Series* or to subscribe to
> the mailing list, please see http://www.ttic.edu/colloquium.php
>
>
> 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, Nov 13, 2022 at 2:48 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:* Monday, November 14th at* 11:30 am CT*
>>
>> *Where: * Talk will be *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=a6540580-fb4e-4402-b1a9-af460182dc1f>*
>> )
>>
>>
>> *Who: * Paul Valiant, Purdue University
>>
>>
>> ------------------------------
>>
>> *Title:* Mean Estimation in Low and High Dimensions
>> *Abstract: *This talk will discuss the fundamental statistical problem
>> of estimating the mean of a distribution, as accurately as possible given
>> samples from it. This problem arises both as a subcomponent of many
>> algorithms, and also in practice as one of the most important data
>> primitives when dealing with real-world data. While many variants and
>> extensions of this problem have been proposed and analyzed, in this talk I
>> will discuss two of the most iconic: 1) when the data comes from a
>> real-valued distribution, and 2) when the data comes from a
>> high-dimensional vector-valued distribution. In both cases, we achieve the
>> first estimators whose accuracy is optimal to 1+o(1) factors, optimal in
>> its dependence on the unknown (co-) variance of the underlying
>> distribution, the number of samples n, and the desired confidence delta. I
>> will highlight some of the crucial and novel analytical tools used in the
>> analysis, and in particular, draw attention to a new "vector Bernstein
>> inequality" which makes precise the intuition that sums of bounded
>> independent random variables in increasingly high dimensions increasingly
>> "adhere to a spherical shell". These results suggest several possible
>> extensions in this large and active area of statistical estimation
>> research. This talk is based on joint work with Jasper C.H. Lee.
>>
>> Host: *Madhur Tulsiani* <madhurt at ttic.edu>
>>
>> For more information on the *C**olloquium** Series* or to subscribe to
>> the mailing list, please see http://www.ttic.edu/colloquium.php
>>
>>
>> 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, Nov 7, 2022 at 10:03 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:* Monday, November 14th at* 11:30 am CT*
>>>
>>> *Where: * Talk will be *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=a6540580-fb4e-4402-b1a9-af460182dc1f>*
>>> )
>>>
>>>
>>> *Who: * Paul Valiant, Purdue University
>>>
>>>
>>> ------------------------------
>>>
>>> *Title:* Mean Estimation in Low and High Dimensions
>>> *Abstract: *This talk will discuss the fundamental statistical problem
>>> of estimating the mean of a distribution, as accurately as possible given
>>> samples from it. This problem arises both as a subcomponent of many
>>> algorithms, and also in practice as one of the most important data
>>> primitives when dealing with real-world data. While many variants and
>>> extensions of this problem have been proposed and analyzed, in this talk I
>>> will discuss two of the most iconic: 1) when the data comes from a
>>> real-valued distribution, and 2) when the data comes from a
>>> high-dimensional vector-valued distribution. In both cases, we achieve the
>>> first estimators whose accuracy is optimal to 1+o(1) factors, optimal in
>>> its dependence on the unknown (co-) variance of the underlying
>>> distribution, the number of samples n, and the desired confidence delta. I
>>> will highlight some of the crucial and novel analytical tools used in the
>>> analysis, and in particular, draw attention to a new "vector Bernstein
>>> inequality" which makes precise the intuition that sums of bounded
>>> independent random variables in increasingly high dimensions increasingly
>>> "adhere to a spherical shell". These results suggest several possible
>>> extensions in this large and active area of statistical estimation
>>> research. This talk is based on joint work with Jasper C.H. Lee.
>>>
>>> Host: *Madhur Tulsiani* <madhurt at ttic.edu>
>>>
>>> For more information on the *C**olloquium** Series* or to subscribe to
>>> the mailing list, please see http://www.ttic.edu/colloquium.php
>>>
>>>
>>>
>>>
>>> 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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