Ιαν
11
2023
By stratis
Κατηγορία:
Τίτλος: From Column Subset to Job Selection and beyond
Ομιλητής: Χρήστος Μπουτσίδης, Vice President and Technology Fellow at Goldman Sachs, New York
Ημερομηνία-χώρος: Παρασκευή 13 Ιανουαρίου, 3-5μμ, ΤΜΗΥΠ, αμφιθέατρο Γ
Περίληψη: The Column Subset Selection Problem (CSSP) is defined as the following combinatorial optimization problem on matrices: given an m x n matrix A and a sampling parameter k < n, select k columns from A to construct an m x k matrix C such that the low-rank matrix reconstruction error of the residual A - CC^{+}A is minimized among all possible choices for the m x k matrix C (here, C^{+}, a k x m matrix, denotes the pseudo-inverse of C). First, we present the state-of-the-art algorithmic results for the CSSP. Next, we discuss two applications of the CSSP: distributed PCA and sparse PCA. We will conclude this talk with a quick overview of the speaker's work on Knowledge Graphs.
Σχετικά με τον ομιλητή: Christos Boutsidis is a Vice President and a Technology Fellow at Goldman Sachs, in New York City. His team, consisting of software engineers and scientists, solve large scale knowledge graph problems, helping Compliance, Investment Banking, and Trading operations, to name a few. Before that, Christos was a Research Scientist with the Scalable Machine Learning Group of Yahoo Research in New York and a Research Staff Member with the Mathematical Sciences Department of the IBM T. J. Watson Research Center in Yorktown Heights, NY. Dr. Boutsidis earned a Ph.D. in Computer Science from Rensselaer Polytechnic Institute in May of 2011 and a BS in Computer Engineering from the University of Patras, in Greece in July of 2006. Dr Boutsidis has published over 30 articles in conferences and journals in algorithms, machine learning, and statistical data analysis.