16 citations · 25 across the 17 of their papers we have counts for
17 papers
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…
Vector-Valued Least-Squares Regression under Output Regularity Assumptions
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
We propose and analyse a reduced-rank method for solving least-squares regression problems with infinite dimensional output. We derive learning bounds for our method, and study und…
Functional Output Regression with Infimal Convolution: Exploring the Huber and -insensitive Losses
Alex Lambert, Dimitri Bouche, Zoltan Szabo +1
The focus of the paper is functional output regression (FOR) with convoluted losses. While most existing work consider the square loss setting, we leverage extensions of the Huber…
Fast Kernel Methods for Generic Lipschitz Losses via -Sparsified Sketches
Tamim El Ahmad, Pierre Laforgue, Florence d'Alché-Buc
Kernel methods are learning algorithms that enjoy solid theoretical foundations while suffering from important computational limitations. Sketching, which consists in looking for s…
Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection
Dimitri Bouche, Rémi Flamary, Florence d'Alché-Buc +4
We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for these quantities are crucial to mitigate the negative eff…
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi +2
This paper tackles post-hoc interpretability for audio processing networks. Our goal is to interpret decisions of a network in terms of high-level audio objects that are also liste…
Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters
Luc Brogat-Motte, Rémi Flamary, Céline Brouard +2
This paper introduces a novel and generic framework to solve the flagship task of supervised labeled graph prediction by leveraging Optimal Transport tools. We formulate the proble…
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions
Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo +2
The design of a metric between probability distributions is a longstanding problem motivated by numerous applications in Machine Learning. Focusing on continuous probability distri…
Emotion Transfer Using Vector-Valued Infinite Task Learning
Alex Lambert, Sanjeel Parekh, Zoltán Szabó +1
Style transfer is a significant problem of machine learning with numerous successful applications. In this work, we present a novel style transfer framework building upon infinite…
Learning Output Embeddings in Structured Prediction
Luc Brogat-Motte, Alessandro Rudi, Céline Brouard +2
A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means…
When OT meets MoM: Robust estimation of Wasserstein Distance
Guillaume Staerman, Pierre Laforgue, Pavlo Mozharovskyi +1
Issued from Optimal Transport, the Wasserstein distance has gained importance in Machine Learning due to its appealing geometrical properties and the increasing availability of eff…
Nonlinear Functional Output Regression: a Dictionary Approach
Dimitri Bouche, Marianne Clausel, François Roueff +1
To address functional-output regression, we introduce projection learning (PL), a novel dictionary-based approach that learns to predict a function that is expanded on a dictionary…