“Unsupervised Domain Adaptation with Copula Models” presented at MLSP’17

Our paper “Unsupervised Domain Adaptation with Copula Models”[1] was presented this week at the IEEE Int’l Workshop on Machine Learning for Signal Processing in Tokyo, Japan.

It was an exciting meeting; unlike the now mega-conferences of CVPR and NIPS kind, MLSP is still refreshingly small.  There were several outstanding tutorials and keynotes by Kenji Fukumizu, Shun-ichi Amari, and Yee Whye Teh, with limited emphasis on “deep.”  Well organized!

Then, there was Tokyo itself.  Always a pleasure to visit.


[1] C. D. Tran, O. Rudovic, and V. Pavlovic, “Unsupervised domain adaptation with copula models,” in IEEE Int’l Conf. Machine Learning for Signal Processing (MLSP), 2017.
author = {Cuong D. Tran and Ognjen Rudovic and Vladimir Pavlovic},
title = {Unsupervised domain adaptation with copula models},
booktitle = {IEEE Int’l Conf. Machine Learning for Signal Processing (MLSP)},
year = {2017},
note = {33\% contribution.},
date-added = {2017-01-17 15:43:46 +0000},
date-modified = {2017-01-17 15:45:08 +0000},
keywords = {domain adaptation, mlsp17},

Chapter on “Machine Learning Methods for Social Signal Procesing” in Social Signal Processing book

Social Signal Processing book by Cambridge University Press was finally published this July.   Read our chapter on “Machine Learning Methods for Social Signal Processing”[1] on p. 234 of this collection of outstanding articles.


[1] O. Rudovic, M. Nicolaou, and V. Pavlovic, “Social Signal Processing,” , A. Vinciarelli, J. Burgoon, N. Magnenat-Thalmann, and M. Pantic, Eds., Cambridge University Press, 2017.
Author = {Ognjen Rudovic and Mihalis Nicolaou and Vladimir Pavlovic},
Chapter = {Machine Learning Methods for Social Signal Processing},
Editor = {Alessandro Vinciarelli and Judee Burgoon and Nadia Magnenat-Thalmann and Maja Pantic},
Note = {33\% contribution},
Publisher = {Cambridge University Press},
Title = {Social Signal Processing},
Year = {2017}}