Tue, Oct 30, 2018 @ 03:30 PM - 04:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nina Balcan, Carnegie Mellon University
Talk Title: Data Driven Algorithm Design
Series: Computer Science Distinguished Lecture Series
Abstract: Data driven algorithm design for combinatorial problems is an important aspect of modern data science and algorithm design. Rather than using off the shelf algorithms that only have worst case performance guarantees, practitioners typically optimize over large families of parametrized algorithms and tune the parameters of these algorithms using a training set of problem instances from their domain to determine a configuration with high expected performance over future instances. However, most of this work comes with no performance guarantees. The challenge is that for many combinatorial problems, including partitioning and subset selection problems, a small tweak to the parameters can cause a cascade of changes in the algorithm\'s behavior, so the algorithm\'s performance is a discontinuous function of its parameters.
In this talk, I will present new work that helps put data driven combinatorial algorithm selection on firm foundations. We provide strong computational and statistical performance guarantees for several subset selection and combinatorial partitioning problems (including various forms of clustering), both for the batch and online scenarios where a collection of typical problem instances from the given application are presented either all at once or in an online fashion, respectively.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Maria Florina Balcan is an Associate Professor in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning, computational aspects in economics and game theory, and algorithms. Her honors include the CMU SCS Distinguished Dissertation Award, an NSF CAREER Award, a Microsoft Faculty Research Fellowship, a Sloan Research Fellowship, and several paper awards. She was a program committee co-chair for the Conference on Learning Theory in 2014 and for the International Conference on Machine Learning in 2016. She is currently board member of the International Machine Learning Society (since 2011), a Tutorial Chair for ICML 2019, and a Workshop Chair for FOCS 2019.
Host: Computer Science Department
Audiences: Everyone Is Invited
Contact: Computer Science Department