WE2.AUD.2
A unified framework for hard and soft clustering with regularized optimal transport
Jean-Frédéric Diebold, Safran Tech, France; Nicolas Papadakis, CNRS, France; Arnaud Dessein, Back Market, France; Charles-Alban Deledalle, Brain Corp, United States
Session:
WE2.AUD: Learning Theory and Methods Lecture
Track:
SiG-DML - Signal and Data Analytics for Machine Learning
Location:
Auditorium
Presentation Time:
Wed, 28 Aug, 14:20 - 14:40 France Time (UTC +1)
Session Chair:
Filip Elvander, Aalto University
Presentation
Discussion
Resources
No resources available.
Session WE2.AUD
WE2.AUD.1: GRADIENT CODING IN DECENTRALIZED LEARNING FOR EVADING STRAGGLERS
Chengxi Li, Mikael Skoglund, KTH Royal Institute of Technology, Sweden
WE2.AUD.2: A unified framework for hard and soft clustering with regularized optimal transport
Jean-Frédéric Diebold, Safran Tech, France; Nicolas Papadakis, CNRS, France; Arnaud Dessein, Back Market, France; Charles-Alban Deledalle, Brain Corp, United States
WE2.AUD.3: Asymptotic Bayes risk of semi-supervised learning with uncertain labeling
Victor Léger, Romain Couillet, Université Grenoble Alpes, France
WE2.AUD.4: On Transfer in Classification: How Well do Subsets of Classes Generalize?
Raphael Baena, enpc, France; Lucas Drumetz, Vincent Gripon, IMT Atlantique, France
WE2.AUD.5: Unpaired training for AFM image processing of R2R-printed CNTs
Soobin Park, Soyoung Na, Seung Hyun Song, Eunju Cha, Sookmyung Women’s University, Korea (South)