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PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Machine Learning Techniques for Predicting Molecular Properties
DTSTART:20150706T153000
DTEND:20150706T173000
DTSTAMP:20260407T101145Z
UID:6fa359a2058b29464165ed63e5486f3ebb15b6f8682d694098a00ca0
CATEGORIES:Conferences - Seminars
DESCRIPTION:Viviana Petrescu\nEDIC Candidacy Exam:\nExam president: Prof. 
 Jean Philippe Thiran\nThesis director: Prof. Volkan Cevher\nThesis codirec
 tor: Prof. Christoph Koch\nCo-examiner: Dr Pearl Pu\nResearch ProposalInfo
 rmation-Theoretic Regret Bounds for Gaussian Process Optimization in the B
 andit Setting by Niranjan Srinivas\, Andreas Krause\, Sham M. Kakade\, and
  Matthias W. Seeger.Self-taught Learning: Transfer Learning from Unlabeled
  Data by Rajat Raina\, Alexis Battle\, Honglak Lee\, Benjamin Packer and A
 ndrew Y. Ng.Learning Invariant Representations of Molecules for Atomizatio
 n Energy Prediction by Grégoire Montavon\, et al.
LOCATION:ELE242 http://plan.epfl.ch/?lang=en&room=ELE+242
STATUS:CONFIRMED
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