[Colloquium] REMINDER: 9/17 TTIC Colloquium: Rebecca Willett, University of Chicago

[Mary Marre via Colloquium]

*9/17 TTIC Colloquium: Rebecca Willett, University of Chicago*




*When:    *  Monday, September 17th at *11:00 am*



*Where:     *TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526



*Who:        *Rebecca Willett, University of Chicago



*Title:*        Learning from Highly Correlated Features using Graph Total
Variation

*Abstract:*
Sparse models for machine learning have received substantial attention over
the past two decades. Model selection, or determining which
features are the best explanatory variables, is critical to theinterpretability
of a learned model. Much of this work assumes that
features are only mildly correlated. However, in modern applications ranging
from functional MRI to genome-wide association studies, we
observe highly correlated features that do not exhibit key properties (such
as the restricted eigenvalue condition). In this talk, I will
describe novel methods for robust sparse linear regression in these settings.
Using side information about the strength of correlations
among features, we form a graph with edge weights corresponding to pairwise
correlations. This graph is used to define a graph total
variation regularizer that promotes similar weights for highly correlated
features. I will show how the graph structure encapsulated
by this regularizer helps precondition correlated features to yield provably
accurate estimates. The proposed approach outperforms several
previous approaches in a variety of experiments on simulated and real fMRI
data.

This is joint work with Yuan Li, Ben Mark, and Garvesh Raskutti.



Host:  Karen Livescu <klivescu at ttic.edu>



For more information on the colloquium series or to subscribe to the
mailing list,please see http://www.ttic.edu/colloquium.php




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Sun, Sep 16, 2018 at 6:45 PM, Mary Marre <mmarre at ttic.edu> wrote:

> *9/17 TTIC Colloquium: Rebecca Willett, University of Chicago*
>
>
>
>
> *When:    *  Monday, September 17th at *11:00 am*
>
>
>
> *Where:     *TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who:        *Rebecca Willett, University of Chicago
>
>
>
> *Title:*        Learning from Highly Correlated Features using Graph Total
>  Variation
>
> *Abstract:*
> Sparse models for machine learning have received substantial attention over
> the past two decades. Model selection, or determining which
> features are the best explanatory variables, is critical to theinterpretability
> of a learned model. Much of this work assumes that
> features are only mildly correlated. However, in modern applications ranging
> from functional MRI to genome-wide association studies, we
> observe highly correlated features that do not exhibit key properties (such
> as the restricted eigenvalue condition). In this talk, I will
> describe novel methods for robust sparse linear regression in these settings.
> Using side information about the strength of correlations
> among features, we form a graph with edge weights corresponding to
> pairwise correlations. This graph is used to define a graph total
> variation regularizer that promotes similar weights for highly correlated
> features. I will show how the graph structure encapsulated
> by this regularizer helps precondition correlated features to yield provably
> accurate estimates. The proposed approach outperforms several
> previous approaches in a variety of experiments on simulated and real fMRI
> data.
>
> This is joint work with Yuan Li, Ben Mark, and Garvesh Raskutti.
>
>
>
> Host:  Karen Livescu <klivescu at ttic.edu>
>
>
>
> For more information on the colloquium series or to subscribe to the
> mailing list,please see http://www.ttic.edu/colloquium.php
>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Mon, Sep 10, 2018 at 12:40 PM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> *9/17 TTIC Colloquium: Rebecca Willett, University of Chicago*
>>
>>
>>
>>
>> *When:    *  Monday, September 17th at 11:00 am
>>
>>
>>
>> *Where:     *TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>>
>>
>> *Who:        *Rebecca Willett, University of Chicago
>>
>>
>>
>> *Title:*        Learning from Highly Correlated Features using Graph
>> Total Variation
>>
>> *Abstract:*
>> Sparse models for machine learning have received substantial attention over
>> the past two decades. Model selection, or determining which
>> features are the best explanatory variables, is critical to theinterpretability
>> of a learned model. Much of this work assumes that
>> features are only mildly correlated. However, in modern applications ranging
>> from functional MRI to genome-wide association studies, we
>> observe highly correlated features that do not exhibit key properties (such
>> as the restricted eigenvalue condition). In this talk, I will
>> describe novel methods for robust sparse linear regression in these settings.
>> Using side information about the strength of correlations
>> among features, we form a graph with edge weights corresponding to
>> pairwise correlations. This graph is used to define a graph total
>> variation regularizer that promotes similar weights for highly correlated
>>  features. I will show how the graph structure encapsulated
>> by this regularizer helps precondition correlated features to yield provably
>> accurate estimates. The proposed approach outperforms several
>> previous approaches in a variety of experiments on simulated and real fMRI
>> data.
>>
>> This is joint work with Yuan Li, Ben Mark, and Garvesh Raskutti.
>>
>>
>>
>> Host:  Karen Livescu <klivescu at ttic.edu>
>>
>>
>>
>> For more information on the colloquium series or to subscribe to the
>> mailing list,please see http://www.ttic.edu/colloquium.php
>>
>>
>>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 517*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757*
>> *f: (773) 357-6970*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
>
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