[Theory] *CANCELED* 4/11 TTIC Colloquium: Elchanan Mossel, MIT

[Mary Marre]

*REMINDER: this talk was CANCELED!*


*When:*         Thursday, April 11, 2024 at* 11:00** am** CT   *


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

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530



*Who: *         Elchanan Mossel, MIT
------------------------------
*Title:*          Reconstructing the Geometry of Random Geometric Graphs

*Abstract:* Random geometric graphs are random graph models defined on
metric spaces. Such a model is defined by first sampling points from a
metric space and then connecting each pair of sampled points with
probability that depends on their distance, independently among pairs. In
this work, we show how to efficiently reconstruct the geometry of the
underlying space from the sampled graph under the manifold assumption,
i.e., assuming that the underlying space is a low dimensional manifold and
that the connection probability is a strictly decreasing function of the
Euclidean distance between the points in a given embedding of the manifold
in . Our work complements a large body of work much of  on manifold
learning, an area of research where TTI played a major role. In Manifold
Learning the goal is to recover a manifold from sampled points sampled in
the manifold along with their (approximate) distances. Based on joint work
with
Han Huang and Pakawut Jiradilok: https://arxiv.org/html/2402.09591v1

* Host: *Liren Shan <http://lirenshan@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, Apr 9, 2024 at 4:46 PM Mary Marre <mmarre at ttic.edu> wrote:

> *Please note that this talk has been CANCELED!*
>
> *When:*         Thursday, April 11, 2024 at* 11:00** am** CT   *
>
>
> *Where:       *Talk will be given *live, in-person* at
>
>                    TTIC, 6045 S. Kenwood Avenue
>
>                    5th Floor, Room 530
>
>
>
>
> *Who: *         Elchanan Mossel, MIT
> ------------------------------
> *Title:*          Reconstructing the Geometry of Random Geometric Graphs
>
> *Abstract:* Random geometric graphs are random graph models defined on
> metric spaces. Such a model is defined by first sampling points from a
> metric space and then connecting each pair of sampled points with
> probability that depends on their distance, independently among pairs. In
> this work, we show how to efficiently reconstruct the geometry of the
> underlying space from the sampled graph under the manifold assumption,
> i.e., assuming that the underlying space is a low dimensional manifold and
> that the connection probability is a strictly decreasing function of the
> Euclidean distance between the points in a given embedding of the manifold
> in . Our work complements a large body of work much of  on manifold
> learning, an area of research where TTI played a major role. In Manifold
> Learning the goal is to recover a manifold from sampled points sampled in
> the manifold along with their (approximate) distances. Based on joint work
> with
> Han Huang and Pakawut Jiradilok: https://arxiv.org/html/2402.09591v1
>
> * Host: *Liren Shan <http://lirenshan@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, Apr 8, 2024 at 2:23 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*         Thursday, April 11, 2024 at* 11:00** am** CT   *
>>
>>
>> *Where:       *Talk will be given *live, in-person* at
>>
>>                    TTIC, 6045 S. Kenwood Avenue
>>
>>                    5th Floor, Room 530
>>
>>
>>
>>
>> *Who: *         Elchanan Mossel, MIT
>> ------------------------------
>> *Title:*          Reconstructing the Geometry of Random Geometric Graphs
>>
>> *Abstract:* Random geometric graphs are random graph models defined on
>> metric spaces. Such a model is defined by first sampling points from a
>> metric space and then connecting each pair of sampled points with
>> probability that depends on their distance, independently among pairs. In
>> this work, we show how to efficiently reconstruct the geometry of the
>> underlying space from the sampled graph under the manifold assumption,
>> i.e., assuming that the underlying space is a low dimensional manifold and
>> that the connection probability is a strictly decreasing function of the
>> Euclidean distance between the points in a given embedding of the manifold
>> in . Our work complements a large body of work much of  on manifold
>> learning, an area of research where TTI played a major role. In Manifold
>> Learning the goal is to recover a manifold from sampled points sampled in
>> the manifold along with their (approximate) distances. Based on joint work
>> with
>> Han Huang and Pakawut Jiradilok: https://arxiv.org/html/2402.09591v1
>>
>> * Host: *Liren Shan <http://lirenshan@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/20240411/6a0c0d70/attachment-0001.html>