Tue PM2.L4.3
Probabilistic Semi-Nonnegative Matrix Factorization via Maximum-Likelihood and Variational Inference
Junbin Liu, Mingjie Shao, Wing-Kin Ma, The Chinese University of Hong Kong, China
Session:
Tue PM2.L4: Learning Theory and Algorithms II Lecture
Track:
SiG-DML - Signal and Data Analytics for Machine Learning
Location:
Nautica
Presentation Time:
Tue, 5 Sep, 17:20 - 17:40 Finland Time (UTC +3)
Session Chair:
Nikos Deligiannis, Vrije Universiteit Brussel
Presentation
Discussion
Resources
No resources available.
Session Tue PM2.L4
Tue PM2.L4.1: Regularization Trade-offs with Fake Features
Martin Hellkvist, Ayca Özcelikkale, Anders Ahlén, Uppsala University, Sweden
Tue PM2.L4.2: ASYMPTOTIC AND NON-ASYMPTOTIC RATE-LOSS BOUNDS FOR LINEAR REGRESSION WITH SIDE INFORMATION
Jiahui WEI, University of Rennes, INSA Rennes, France; Elsa DUPRAZ, IMT-Atlantique, France; Philippe MARY, University of Rennes, INSA Rennes, France
Tue PM2.L4.3: Probabilistic Semi-Nonnegative Matrix Factorization via Maximum-Likelihood and Variational Inference
Junbin Liu, Mingjie Shao, Wing-Kin Ma, The Chinese University of Hong Kong, China
Tue PM2.L4.4: DOMAIN AND MODALITY ADAPTATION USING MULTI-KERNEL MATCHING
Tamir Baruch Yampolsky, Ronen Talmon, Technion – Israel Institute of Technology, Israel; Ofir Lindenbaum, Bar-Ilan University, Israel
Tue PM2.L4.5: Identifiability of Polytopic Matrix Factorization
Olivier Vu Thanh, Nicolas Gillis, University of Mons, Belgium