16 citations · 25 across the 17 of their papers we have counts for
6 papers · 1 filter
Fast kernel half-space depth for data with non-convex supports
Arturo Castellanos, Pavlo Mozharovskyi, Florence d'Alché-Buc +1
Data depth is a statistical function that generalizes order and quantiles to the multivariate setting and beyond, with applications spanning over descriptive and visual statistics,…
Abnormal component analysis
Romain Valla, Pavlo Mozharovskyi, Florence d'Alché-Buc
At the crossway of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behaviour. Be it measurement errors, disease develop…
Tailoring Mixup to Data for Calibration
Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
Among all data augmentation techniques proposed so far, linear interpolation of training samples, also called Mixup, has found to be effective for a large panel of applications. Al…
Exploiting Edge Features in Graphs with Fused Network Gromov-Wasserstein Distance
Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc
Pairwise comparison of graphs is key to many applications in Machine learning ranging from clustering, kernel-based classification/regression and more recently supervised graph pre…
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi +2
This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to inter…
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue +1
Leveraging the kernel trick in both the input and output spaces, surrogate kernel methods are a flexible and theoretically grounded solution to structured output prediction. If the…