Συντάχθηκε 18-06-2018 18:26
από Antonios Deligiannakis
Email συντάκτη: adeligiannakis<στο>tuc.gr
Ενημερώθηκε:
-
Ιδιότητα: ΔΕΠ ΗΜΜΥ.
Ημερομηνία: Τετάρτη 20/6/2018
Ώρα: 3:00 μ.μ.
Αίθουσα: Αμφιθέατρο του Κτ. Επιστημών
Title: Numerical Optimization, Formulations, Serial and Parallel Algorithms for Machine Learning and Network Science
Speaker: Kimon Fountoulakis
Postdoctoral Fellow
University of California Berkeley
http://www1.icsi.berkeley.edu/~kfount/
Abstract:
In this talk we will discuss optimization formulations and algorithms for
applications that arise in machine learning and network science. Large
scale data are becoming increasingly abundant for both commercial and
scientific usage. This is motivated by easier means of gathering data from
diverse sources. The sheer size of the data necessitates the need for
developing efficient optimization formulations, serial and parallel
optimization algorithms, and easy to use software in order to reduce the
running time of processing the data. Some examples where processing of
large scale data is useful are prediction and pattern recognition tasks in
machine learning and network science, i.e., regression, classification,
clustering and community detection. These tasks are tightly connected to
numerical optimization and high performance computing. For example,
regression tasks such as linear regression and logistic regression are
often formulated as unconstrained convex optimization problems. While
clustering or community detection problems are often formulated as NP-hard
combinatorial problems which are then relaxed and posed as convex problems
such as linear, quadratic programming. We will demonstrate worst-case
running times as well as practical performance of state-of-the-art
optimization algorithms for these problems.
Short CV:
Kimon Fountoulakis is a postdoctoral fellow and co-PI at the University of
California Berkeley and the International Computer Science Institute
where he has been a member since 2015. Kimon completed his MSc and PhD at
the Department of Mathematics at the University of Edinburgh both in the
field of numerical optimization. Kimon’s most recent work focuses on
scalable graph analytics and in particular the application of large scale
optimization to local graph clustering. He has also worked on
parallelizing first-order optimization methods for local graph clustering
and machine learning problems. During his PhD, Kimon worked on developing
and analyzing second-order optimization methods for machine learning and
signal processing problems.
