Our group meets on Thursdays at 11:00 a.m.
 Fall Semester: Beckett Conf. Rm.
 Spring Semester: Beckett Conf. Rm.
Version:
Wed 6 April 2016
 Jan 14
 No meeting


 Jan 21
 Xinyue Liu
 Kernelized Matrix Factorization for Collaborative Filtering
Matrix factorization (MF) methods have shown great promise in
collaborative filtering (CF). Conventional MF methods usually assume
that the correlated data is distributed on a linear hyperplane, which
is not always the case. Kernel methods are used widely in SVMs to
classify linearly nonseparable data, as well as in PCA to discover
the nonlinear embeddings of data. In this paper, we present a novel
method to kernelize matrix factorization for collaborative filtering,
which is equivalent to performing the lowrank matrix factorization in
a possibly much higher dimensional space that is implicitly defined by
the kernel function. Inspired by the success of multiple kernel
learning (MKL) methods, we also explore the approach of learning
multiple kernels from the rating matrix to further improve the
accuracy of prediction. Since the right choice of kernel is usually
unknown, our proposed multiple kernel matrix factorization method
helps to select effective kernel functions from the
candidates. Through extensive experiments on realworld datasets, we
show that our proposed method captures the nonlinear correlations
among data, which results in improved prediction accuracy compared to
the stateofart CF models.
 Jan 28
 Carolina Ruiz
 Deviationbased dynamic time warping for clustering human sleep
In this talk, we propose two versions of a modified dynamic time warping
approach for comparing discrete time series. This approach is motivated by the
observation that the distribution of dynamic time warping paths between pairs
of human sleep time series is concentrated around the path of constant slope.
Both versions use a penalty term for the deviation between the warping path and
the path of constant slope for a given pair of time series. In the first
version, global weighted dynamic time warping, the penalty term is added as a
postprocessing step after a standard dynamic time warping computation, yielding
a modified similarity metric that can be used for time series clustering. The
second version, stepwise deviationbased dynamic time warping, incorporates the
penalty term into the dynamic programming optimization itself, yielding
modified optimal warping paths, together with a similarity metric. Clustering
experiments over synthetic data, as well as over human sleep data, show that
the proposed methods yield significantly improved accuracy and generative log
likelihood as compared with standard dynamic time warping.
 Feb 4
 tbd
 tbd

 Feb 11
 tbd
 tbd

 Feb 18
 Academic Advising Appointment Day
 No AIRG Meeting

 Feb 25
 tbd
 tbd

 March 3
 tbd
 tbd

 Mar 10
 Ugrad & Grad student break
 Mar 17
 tbd
 tbd

 Mar 24
 tbd
 tbd

 Mar 31
 POSTPONED until 7th April
 John Boaz Lee
 Network Lasso: Clustering and Optimization in Large Graphs

 Apr 7
 John Boaz Lee
 Network Lasso: Clustering and Optimization in Large Graphs
Convex optimization is an essential tool for modern data analysis, as
it provides a framework to formulate and solve many problems in
machine learning and data mining. However, general convex optimization
solvers do not scale well, and scalable solvers are often specialized
to only work on a narrow class of problems. Therefore, there is a need
for simple, scalable algorithms that can solve many common
optimization problems. In this paper, we introduce the network lasso,
a generalization of the group lasso to a network setting that allows
for simultaneous clustering and optimization on graphs. We develop an
algorithm based on the Alternating Direction Method of Multipliers
(ADMM) to solve this problem in a distributed and scalable manner,
which allows for guaranteed global convergence even on large
graphs. We also examine a nonconvex extension of this approach. We
then demonstrate that many types of problems can be expressed in our
framework. We focus on three in particularbinary classification,
predicting housing prices, and event detection in time series
datacomparing the network lasso to baseline approaches and showing
that it is both a fast and accurate method of solving large
optimization problems.
 Apr 14
 Yan Wang
 Student modeling from different aspects
 MS Thesis presentation
 Advisor: Neil Heffernan
With the wide usage of online tutoring systems, researchers become
interested in mining data from logged files of these systems, so as to
get better understanding of students. Varieties of aspects of
students' learning have become focus of studies, such as modeling
students. mastery status and affects. On the other hand, Randomized
Controlled Trial (RCT), which is an unbiased method for getting
insights of education, finds its way in Intelligent Tutoring
System. Firstly, people are curious about what kind of settings would
work better. Secondly, such a tutoring system, with lots of students
and teachers using it, provides an opportunity for building a RCT
infrastructure underlying the system. With the increasing interest in
Data mining and RCT, the thesis focuses on these two aspects. In the
first part, we focus on analyzing and mining data from ASSISTments, an
online tutoring system run by a team in Worcester Polytechnic
Institute. Through the data, we try to answer several questions from
different aspects of students learning. The first question we try to
answer is what matters more to student modeling, skill information or
student information. The second question is whether it is necessary to
model students. learning at different opportunity count. The third
question is about the benefits of using partial credit, rather than
binary credit as measurement of students. learning in RCT. The fourth
question focuses on the amount that students spent Wheel Spinning in
the tutoring system. The fifth questions studies the tradeoff between
the mastery threshold and the time spent in the tutoring system. By
answering the five questions, we both propose machine learning
methodology that can be applied in educational data mining, and
present findings from analyzing and mining the data. In the second
part, we focused on RCTs within ASSISTments. Firstly, we looked at a
pilot study of reassessment and relearning, which suggested a better
system setting to improve students. robust learning. Secondly, we
proposed the idea to build an infrastructure of learning within
ASSISTments, which provides the opportunities to improve the whole
educational environment.
 Apr 21
 Undergraduate Project Presentation Day
 No AIRG Meeting
 Apr 28
 (reserved for possible MS thesis presentations)


