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CREATED:20230605T101513Z
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SUMMARY:Ομιλία για "Learning From Rankings" 
 του Στρατή Ιωαννίδη | 06.06.2023, 12
 :00
LOCATION:Λ - Κτίριο Επιστημών/ΗΜΜΥ, 145Π-58
DESCRIPTION:https://www.tuc.gr/el/to-polytechnei
 o/ilektronikes-ypiresies/imerologio/
 imerologio-ekdiloseon-1?tx_tucevents
 2_tuceventsdisplay%5Baction%5D=show&
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 entsdisplay%5Bevent%5D=6226&cHash=13
 3b75258fb736487102ea1b41fefded\nΤίτλ
 ος\n Learning from Rankings\n \n Περ
 ίληψη\n We consider learning from ra
 nkings, i.e., learning from a datase
 t containing subsets of samples rank
 ed w.r.t. their relative order. For 
 example, a medical expert presented 
 with patient records can order them 
 w.r.t. the relative severity of a di
 sease. Rankings are often less noisy
  than class labels: human experts di
 sagreeing when generating class judg
 ments often exhibit reduced variabil
 ity when asked to compare samples in
 stead. Rankings are also more inform
 ative, as they capture both inter an
 d intra-class relationships; the lat
 ter are not revealed via class label
 s alone. Nevertheless, the combinato
 rial nature of rankings increases th
 e computational cost of training sig
 nificantly. We propose spectral algo
 rithms to accelerate training in thi
 s ranking regression setting; our ma
 in technical contribution is to show
  that the Plackett-Luce negative log
 -likelihood augmented with a proxima
 l penalty has stationary points that
  satisfy the balance equations of a 
 Markov Chain. This observation yield
 s fast spectral algorithms for ranki
 ng regression for both shallow and d
 eep neural network regression models
 . Compared to state-of-the-art siame
 se networks, our resulting algorithm
 s are up to 175 times faster and att
 ain better predictions by up to 26% 
 Top-1 Accuracy and 6% Kendall-Tau co
 rrelation over five real-life rankin
 g datasets.\n  \n Βιογραφικό\n Strat
 is Ioannidis is an associate profess
 or in the Electrical and Computer En
 gineering Department of Northeastern
  University, in Boston, MA, where he
  also holds a courtesy appointment w
 ith the Khoury College of Computer S
 ciences. He received his B.Sc. (2002
 ) in Electrical and Computer Enginee
 ring from the National Technical Uni
 versity of Athens, Greece, and his M
 .Sc. (2004) and Ph.D. (2009) in Comp
 uter Science from the University of 
 Toronto, Canada. Prior to joining No
 rtheastern, he was a research scient
 ist at the Technicolor research cent
 ers in Paris, France, and Palo Alto,
  CA, as well as at Yahoo Labs in Sun
 nyvale, CA. He is the recipient of a
 n NSF CAREER Award, a Google Faculty
  Research Award, a Facebook Research
  Award, a Martin W. Essigmann Outsta
 nding Teaching Award, and several be
 st paper awards. His research intere
 sts span machine learning, distribut
 ed systems, networking, optimization
 , and privacy.\n
STATUS:CONFIRMED
ORGANIZER;RSVP=FALSE;CN=TUC;CUTYPE=TUC:mailto:webmaster@tuc.gr
DTSTART:20230606T120000
DTEND:20230606T130000
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