Thesis Proposal - 10/27/14 - Madalina Fiterau - "Discovering Compact and Informative Structures through Data Partitioning"

[Artur Dubrawski]

Team,

If you're available please come and cheer for Ina on Monday.

Artur

---------------------------- Original Message ----------------------------
Subject: Thesis Proposal - 10/27/14 - Madalina Fiterau - "Discovering
Compact and Informative Structures through Data Partitioning"
From:    "Diane Stidle" <diane+ at cs.cmu.edu>
Date:    Wed, October 22, 2014 4:42 pm
To:      ML-SEMINAR at cs.cmu.edu
--------------------------------------------------------------------------

/Thesis Proposal.../

Date: 10/27/14
Time: 9:00am
Place: 8102 GHC
Speaker: Madalina Fiterau

Title: Discovering Compact and Informative Structures through Data
Partitioning

Abstract:

In many practical scenarios, complex high-dimensional data contains
low-dimensional structures that could be informative of the analytic
problems at hand.

My thesis aims at detecting such structures, if they exist, and at using
them to construct compact interpretable models for different machine
learning tasks that can benefit various practical applications.

We formalize the problem of Informative Projection Recovery. It is the
problem of extracting a small set of low-dimensional projections of data
which jointly support an accurate solution to a given learning task. Our
solution to this problem is a regression-based algorithm that identifies
informative projections by optimizing over a matrix of point-wise loss
estimators. It generalizes to multiple types of machine learning problems,
offering solutions to classification, clustering, regression, and active
learning tasks. Experiments show that our method can discover and leverage
low-dimensional structures in data, yielding accurate and compact models.
Our method is particularly useful in applications involving multivariate
numeric data in which expert assessment of the results is of the essence.

The focus of our forthcoming research is on cost-sensitive feature
selection put in the context of the underlying compact structures in data.
We consider the process used to generate the features, as well as their
reliability and interdependence to reduce the overall cost. Typically, our
applications rely on a core set of features obtained through expensive
measurements, enhanced using transformations derived from one or several
core features. Our preliminary results show that leveraging
low-dimensional structures may yield more powerful models without an
increase in the cost of feature acquisition. The crux of our proposed
technique is to leverage the submodular cost and the redundancy of the
features by generating penalties according to the structure of their
dependency graph. We will then develop online, adaptive policy-learning
optimization procedures for feature selection with submodular cost
constraints. We will first consider the batch mode setting and learn a
model that maps samples to the appropriate feature subset, achievable by
maximizing a submodular objective. The aim is to then efficiently update
this mapping as more data becomes available, the main challenge being the
trade-off between flexibility and robustness. We also plan to develop
temporally-aware models which would dynamically adjust the selection of
low-dimensional structures as the underlying data distribution varies over
time.

Thesis committee:
Artur Dubrawski, Chair
Geoff Gordon
Alex Smola
Andreas Krause (ETH Zurich)

Link to the proposal document:
www.cs.cmu.edu/~mfiterau/proposal/proposal.pdf

-- 
Diane Stidle
Graduate Programs Manager
Machine Learning Department
Carnegie Mellon University
diane at cs.cmu.edu
412-268-1299




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