Fwd: Reminder - Thesis Defense - July 30th - Eric Lei - Multi-view Relationships for Analytics and Inference

[Artur Dubrawski]

Just a reminder, see you there!

Artur

---------- Forwarded message ---------
From: Diane Stidle <stidle at andrew.cmu.edu>
Date: Tue, Jul 30, 2019, 9:28 AM
Subject: Reminder - Thesis Defense - July 30th - Eric Lei - Multi-view
Relationships for Analytics and Inference
To: ml-seminar at cs.cmu.edu <ML-SEMINAR at cs.cmu.edu>, Mario Berges <
marioberges at cmu.edu>, <labov1 at llnl.gov>


*Thesis Defense*

Date: July 30, 2019
Time: 3:00pm
Place: GHC 8102
PhD Candidate: Eric Lei

*Title: **Multi-view Relationships for Analytics and Inference*

Abstract:

An interesting area of machine learning is methods for multi-view data,
relational data whose features have been partitioned. Multi-view learning
exploits relationships between views, giving it certain advantages over
traditional single-view techniques, which may struggle to find these
relationships or only learn them implicitly. These relationships are often
especially salient in understanding the data or performing prediction. This
work explores an underutilized approach in multi-view learning: to focus on
multi-view relationships---the variables that govern relations between
views---themselves as units of analysis.  We investigate how this approach
impacts analytics and inference in ways that standard multi-view and
single-view learning cannot. We hypothesize that by ignoring relations
between views or factoring them in only indirectly, standard approaches
risk overlooking key structure. Accordingly, our goal is to investigate the
extent multi-view relationships can be characterized and employed as units
of analysis in descriptive analytics and inference. We present novel
methods to do so, either using domain knowledge or by learning from data,
which reveal structure that alternative methods do not or have competitive
performance with the state of the art. Empirical results are presented in
several application domains. First, we use domain knowledge to assume a
known form for multi-view relationships in the task of gamma source
detection. We aggregate the views by filtering their inferences
collectively to perform classification. Second, we assume multi-view
relationships are linear and learn them from data in a different approach
toward gamma source detection. Our method detects anomalies when these
relationships are disrupted. Third, we relax the assumption of linearity
and propose a novel clustering method that finds cluster-wise linear
relationships. This method discovers explanatory structure in a medical
problem. Fourth, we extend this method to classification and demonstrate
its superior performance on a load monitoring problem.

*Thesis Committee: *
Artur Dubrawski (Chair)
Barnabas Poczos
Mario Berges
Simon Labov (Lawrence Livermore National Laboratory)

-- 
Diane Stidle
Graduate Programs Manager
Machine Learning Department
Carnegie Mellon Universitystidle at cmu.edu
412-268-1299
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/autonlab-users/attachments/20190730/9bdebcbd/attachment.html>