[Colloquium] 6/2 Talks at TTIC: Sanmi Koyejo, Stanford University

[Mary Marre]

When:     Thursday, June 2nd at 11:00 am

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

Who:        Sanmi Koyejo, Stanford University

Title:        From Probabilistic Models to Decision Theory and Back Again

Abstract:
Decision making is a fundamental aspect of probabilistic inference, and
involves selecting a point estimate from a probabilistic model e.g. a
prediction or a parameter estimate, which optimizes some measure of
utility. In the first part of the talk, I will outline some a new results
on binary and multilabel prediction with complex performance measures, such
as F-measure and Jaccard measure. Perhaps surprisingly, the prediction
which maximizes the utility for a range of such measures takes a
particularly simple form as the thresholding of the probability of the
positive class. This result motivates simple but effective classifiers for
"brain reading" i.e. predicting cognitive processes from fMRI data.

In the second part of the talk, I will discuss a “dual” of the decision
making problem i.e. estimating a probabilistic model that satisfies the
user determined utility. This approach to probabilistic inference may be
employed for incorporating potentially useful prior information encoded in
the user-devised risk function. One useful application of the resulting
procedure is for incorporating structural assumptions on the support of
latent variables. I will outline some new results which show that the
support constrained information projection is submodular, thus can be
efficiently optimized using standard techniques. Of particular interest is
where the the support constraints can be represented as a matroid,
resulting in new techniques for probabilistic (variable/group/tree) sparse
regression. I will discuss a recent application of these techniques to
develop a novel sparse probabilistic canonical correlation analysis for the
joint analysis of fMRI statistical maps and behavioural measurements.


Bio:
Sanmi (Oluwasanmi) Koyejo is an engineering research associate in the
Poldrack Lab at Stanford University and an Assistant Professor in the
Department of Computer Science at the University of Illinois at
Urbana-Champaign. Koyejo’s research involves the development and analysis
of probabilistic & statistical machine learning techniques motivated by,
and applied to various modern big data problems, with a particular focus on
analysis of large scale biological data such as neuroimaging and genetics
data. Koyejo completed his Ph.D in Electrical Engineering at the University
of Texas at Austin under the supervision of Joydeep Ghosh and was a postdoc
with Russell Poldrack and Pradeep Ravikumar. He has been the recipient of
several awards including the outstanding NCE/ECE student award, a best
student paper award from the conference on uncertainty in artificial
intelligence (UAI) and a trainee award from the Organization for Human
Brain Mapping (OHBM).


Host: Srinadh Bhojanapalli, srinadh at ttic.edu




Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Tue, May 31, 2016 at 5:20 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Thursday, June 2nd at 11:00 am
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:        Sanmi Koyejo, Stanford University
>
> Title:        From Probabilistic Models to Decision Theory and Back Again
>
> Abstract:
> Decision making is a fundamental aspect of probabilistic inference, and
> involves selecting a point estimate from a probabilistic model e.g. a
> prediction or a parameter estimate, which optimizes some measure of
> utility. In the first part of the talk, I will outline some a new results
> on binary and multilabel prediction with complex performance measures, such
> as F-measure and Jaccard measure. Perhaps surprisingly, the prediction
> which maximizes the utility for a range of such measures takes a
> particularly simple form as the thresholding of the probability of the
> positive class. This result motivates simple but effective classifiers for
> "brain reading" i.e. predicting cognitive processes from fMRI data.
>
> In the second part of the talk, I will discuss a “dual” of the decision
> making problem i.e. estimating a probabilistic model that satisfies the
> user determined utility. This approach to probabilistic inference may be
> employed for incorporating potentially useful prior information encoded in
> the user-devised risk function. One useful application of the resulting
> procedure is for incorporating structural assumptions on the support of
> latent variables. I will outline some new results which show that the
> support constrained information projection is submodular, thus can be
> efficiently optimized using standard techniques. Of particular interest is
> where the the support constraints can be represented as a matroid,
> resulting in new techniques for probabilistic (variable/group/tree) sparse
> regression. I will discuss a recent application of these techniques to
> develop a novel sparse probabilistic canonical correlation analysis for
> the joint analysis of fMRI statistical maps and behavioural measurements.
>
>
> Bio:
> Sanmi (Oluwasanmi) Koyejo is an engineering research associate in the
> Poldrack Lab at Stanford University and an Assistant Professor in the
> Department of Computer Science at the University of Illinois at
> Urbana-Champaign. Koyejo’s research involves the development and analysis
> of probabilistic & statistical machine learning techniques motivated by,
> and applied to various modern big data problems, with a particular focus on
> analysis of large scale biological data such as neuroimaging and genetics
> data. Koyejo completed his Ph.D in Electrical Engineering at the University
> of Texas at Austin under the supervision of Joydeep Ghosh and was a postdoc
> with Russell Poldrack and Pradeep Ravikumar. He has been the recipient of
> several awards including the outstanding NCE/ECE student award, a best
> student paper award from the conference on uncertainty in artificial
> intelligence (UAI) and a trainee award from the Organization for Human
> Brain Mapping (OHBM).
>
>
> Host: Srinadh Bhojanapalli, srinadh at ttic.edu
>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <%28773%29%20834-1757>*
> *f: (773) 357-6970 <%28773%29%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20160602/dc0242ab/attachment-0001.htm