Asia
Total Least Squares Regression in Input Sparsity Time
Huaian Diao, Zhao Song, David Woodruff, Xin Yang
In the total least squares problem, one is given an m n matrix A, and an m d matrix B, and one seeks to "correct" both A and B, obtaining matrices  and B, so that there exists an X satisfying the equation ÂX = B. Typically the problem is overconstrained, meaning that m max(n, d).
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels
Natalia Neverova, David Novotny, Andrea Vedaldi
Analternativeapproach isto predict instead adistributionp(ˆy|x) = Φˆy(x) over possible values of the annotationy. Theannotators are shown a set of points sampled randomly and uniformly over one of predefined body parts of aperson inan image.
Computing Higher Order Derivatives of Matrix and Tensor Expressions
Soeren Laue, Matthias Mitterreiter, Joachim Giesen
Optimization is an integral part of most machine learning systems and most numerical optimization schemes relyonthecomputation ofderivatives.
d7ce06e9293c3d8e6cb3f80b4157f875-Paper-Conference.pdf
However,computing extendedpersistent homologysummaries remainsslowfor large and dense graphs and can be aserious bottleneck for the learning pipeline.
Unsupervised Scalable Representation Learning for Multivariate Time Series
Jean-Yves Franceschi, Aymeric Dieuleveut, Martin Jaggi
Neural Information Processing Systems http://nips.cc/
