19 citations · 64 across the 31 of their papers we have counts for
4 papers · 1 filter
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko
Self-training is a well-known approach for semi-supervised learning. It consists of iteratively assigning pseudo-labels to unlabeled data for which the model is confident and treat…
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
Quentin Bouniot, Ievgen Redko, Anton Mallasto +6
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explain…
Revisiting invariances and introducing priors in Gromov-Wasserstein distances
Pinar Demetci, Quang Huy Tran, Ievgen Redko +1
Gromov-Wasserstein distance has found many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformati…
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Oliver Struckmeier, Ievgen Redko, Anton Mallasto +3
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle. Despite its widespread use in machine learn…