\u03a3\u03b1\u03c2 \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03bd\u03bf\u03c5\u03bc\u03b5 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03ba\u03ac\u03c4\u03c9 \u03bf\u03bc\u03b9\u03bb\u03af\u03b1 \u03b7 \u03bf\u03c0\u03bf\u03af\u03b1 \u03b8\u03b1 \u03b4\u03bf\u03b8\u03b5\u03af \u03c3\u03c4\u03b1 \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03b5\u03b9\u03c1\u03ac\u03c2 \u03b5\u03ba\u03b4\u03b7\u03bb\u03ce\u03c3\u03b5\u03c9\u03bd “\u03a3\u03b5\u03bc\u03b9\u03bd\u03ac\u03c1\u03b9\u03bf CEID \u03ba\u03b1\u03b9 Social Hour”:<\/p>\n\n\n\n
\u03a4\u03af\u03c4\u03bb\u03bf\u03c2:<\/strong>\u00a0 \u00a0“From Column Subset to Job Selection and \u0392eyond”<\/p>\n\n\n\n \u0397\u03bc\u03b5\u03c1\u03bf\u03bc\u03b7\u03bd\u03af\u03b1-\u03c7\u03ce\u03c1\u03bf\u03c2:<\/strong>\u00a0 \u03a0\u03b1\u03c1\u03b1\u03c3\u03ba\u03b5\u03c5\u03ae, 13 \u0399\u03b1\u03bd\u03bf\u03c5\u03b1\u03c1\u03af\u03bf\u03c5 2023, 15:00, \u03a4\u039c\u0397\u03a5\u03a0, A\u03bc\u03c6\u03b9\u03b8\u03ad\u03b1\u03c4\u03c1\u03bf \u0393<\/p>\n\n\n\n \u03a0\u03b5\u03c1\u03af\u03bb\u03b7\u03c8\u03b7:\u00a0<\/strong> \u03a3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ac \u03bc\u03b5 \u03c4\u03bf\u03bd \u03bf\u03bc\u03b9\u03bb\u03b7\u03c4\u03ae:\u00a0\u00a0<\/strong> \u03a3\u03b1\u03c2 \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03bd\u03bf\u03c5\u03bc\u03b5 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03ba\u03ac\u03c4\u03c9 \u03bf\u03bc\u03b9\u03bb\u03af\u03b1 \u03b7 \u03bf\u03c0\u03bf\u03af\u03b1 \u03b8\u03b1 \u03b4\u03bf\u03b8\u03b5\u03af \u03c3\u03c4\u03b1 \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03b1 \u03c4\u03b7\u03c2 \u03c3\u03b5\u03b9\u03c1\u03ac\u03c2 \u03b5\u03ba\u03b4\u03b7\u03bb\u03ce\u03c3\u03b5\u03c9\u03bd “\u03a3\u03b5\u03bc\u03b9\u03bd\u03ac\u03c1\u03b9\u03bf CEID \u03ba\u03b1\u03b9 Social Hour”: \u03a4\u03af\u03c4\u03bb\u03bf\u03c2:\u00a0 \u00a0“From Column Subset to Job Selection and \u0392eyond” \u0397\u03bc\u03b5\u03c1\u03bf\u03bc\u03b7\u03bd\u03af\u03b1-\u03c7\u03ce\u03c1\u03bf\u03c2:\u00a0 \u03a0\u03b1\u03c1\u03b1\u03c3\u03ba\u03b5\u03c5\u03ae, 13 \u0399\u03b1\u03bd\u03bf\u03c5\u03b1\u03c1\u03af\u03bf\u03c5 2023, 15:00, \u03a4\u039c\u0397\u03a5\u03a0, A\u03bc\u03c6\u03b9\u03b8\u03ad\u03b1\u03c4\u03c1\u03bf \u0393 \u03a0\u03b5\u03c1\u03af\u03bb\u03b7\u03c8\u03b7:\u00a0The Column Subset Selection Problem (CSSP) is defined as the following combinatorial optimization problem on matrices:\u00a0given an […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24],"tags":[],"class_list":["post-766","post","type-post","status-publish","format-standard","hentry","category-24"],"_links":{"self":[{"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=\/wp\/v2\/posts\/766","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=766"}],"version-history":[{"count":1,"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=\/wp\/v2\/posts\/766\/revisions"}],"predecessor-version":[{"id":767,"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=\/wp\/v2\/posts\/766\/revisions\/767"}],"wp:attachment":[{"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=766"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=766"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hsis.upatras.gr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=766"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
The Column Subset Selection Problem (CSSP) is defined as the following combinatorial optimization problem on matrices:\u00a0given 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).\u00a0 First, we present the state-of-the-art algorithmic results for the CSSP. Next,\u00a0we discuss two applications of the CSSP: distributed PCA and sparse PCA. We will conclude this talk with a quick overview\u00a0of the speaker’s work on Knowledge Graphs.\u00a0<\/p>\n\n\n\n
Christos Boutsidis<\/a>\u00a0is a Vice President and a Technology Fellow at Goldman Sachs, in New\u00a0York 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\u00a0published over 30 articles<\/a>\u00a0in conferences and journals in algorithms, machine learning, and statistical data analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"