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SUMMARY:Towards new challenges related to ranking data of complex structur
 e.
DTSTART:20231213T100000
DTEND:20231213T110000
DTSTAMP:20260429T114854Z
UID:1cc28e5e5c043ef3e59aaa6a685c27abcb0fcda52a02be79179b6c2e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Myrto Limnios – Uni. Copenhagen  \nPresentation in Mathemat
 ics\n\nRanking random observations has become essential to many data anal
 ysis problems\, ranging from recommendation systems\, computational biolog
 y\, to information retrieval for instance\, wherein the information acquis
 ition processes nowadays often involve various and poorly controlled sour
 ces\, leading to datasets possibly exhibiting strong sampling bias. Fundam
 ental to learning-to-rank algorithms and nonparametric hypothesis testing 
 when the observations are drawn from multiple independent distributions\, 
 its study in high-dimension is the subject of much attention\, especially 
 due to the lack of natural relation order in the underlying space. \nIn t
 his talk\, we will discuss an approach for ranking random observations of
  complex structure\, when drawn from (two) unknown distributions\, relying
  on a generalization of two-sample linear rank statistics. We will show ho
 w this new class encompasses and naturally extends classic univariate rank
  test statistics\, as well as ranking performance criteria for related alg
 orithms through the concept of Receiver Operating Characteristic (ROC) cur
 ve. Beyond preserving fundamental properties from the univariate setting\,
  we will prove new concentration bounds for collections of rank statistic
 s\, and apply them to developing new methodologies in hypothesis testing w
 ith finite sample guarantees of testing errors. Convincing experimental st
 udies will illustrate the advantages of this approach compared to state-of
 -the art methods. We will finally discuss important challenges for future 
 research on specific structures of data. \n\n 
LOCATION:https://epfl.zoom.us/j/61532230302
STATUS:CONFIRMED
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