204 citations · 324 across the 13 of their papers we have counts for
13 papers
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach
Valérie Beaudouin, Isabelle Bloch, David Bounie +6
The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning. Deep learning methods are remarkably accurate, but also opaque, which limits…
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha +5
One of the challenges in machine learning research is to ensure that presented and published results are sound and reliable. Reproducibility, that is obtaining similar results as p…
Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses
Pierre Laforgue, Alex Lambert, Luc Brogat-Motte +1
Operator-Valued Kernels (OVKs) and associated vector-valued Reproducing Kernel Hilbert Spaces provide an elegant way to extend scalar kernel methods when the output space is a Hilb…
From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining
Alexandre Garcia, Pierre Colombo, Slim Essid +2
The task of predicting fine grained user opinion based on spontaneous spoken language is a key problem arising in the development of Computational Agents as well as in the developm…
Functional Isolation Forest
Guillaume Staerman, Pavlo Mozharovskyi, Stephan Clémençon +1
For the purpose of monitoring the behavior of complex infrastructures (e.g. aircrafts, transport or energy networks), high-rate sensors are deployed to capture multivariate data, g…
A multimodal movie review corpus for fine-grained opinion mining
Alexandre Garcia, Slim Essid, Florence d'Alché-Buc +1
In this paper, we introduce a set of opinion annotations for the POM movie review dataset, composed of 1000 videos. The annotation campaign is motivated by the development of a hie…
A Structured Prediction Approach for Label Ranking
Anna Korba, Alexandre Garcia, Florence d'Alché Buc
We propose to solve a label ranking problem as a structured output regression task. We adopt a least square surrogate loss approach that solves a supervised learning problem in two…
Autoencoding any Data through Kernel Autoencoders
Pierre Laforgue, Stephan Clémençon, Florence d'Alché-Buc
This paper investigates a novel algorithmic approach to data representation based on kernel methods. Assuming that the observations lie in a Hilbert space X, the introduced Kernel…
Infinite-Task Learning with RKHSs
Romain Brault, Alex Lambert, Zoltán Szabó +2
Machine learning has witnessed tremendous success in solving tasks depending on a single hyperparameter. When considering simultaneously a finite number of tasks, multi-task learni…
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Alexandre Garcia, Slim Essid, Chloé Clavel +1
Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to absta…
Random Fourier Features for Operator-Valued Kernels
Romain Brault, Florence d'Alché-Buc, Markus Heinonen
Devoted to multi-task learning and structured output learning, operator-valued kernels provide a flexible tool to build vector-valued functions in the context of Reproducing Kernel…
Learning nonparametric differential equations with operator-valued kernels and gradient matching
Markus Heinonen, Florence d'Alché-Buc
Modeling dynamical systems with ordinary differential equations implies a mechanistic view of the process underlying the dynamics. However in many cases, this knowledge is not avai…
Parametric Estimation of Ordinary Differential Equations with Orthogonality Conditions
Nicolas J-B Brunel, Quentin Clairon, Florence d'Alche-Buc
Differential equations are commonly used to model dynamical deterministic systems in applications. When statistical parameter estimation is required to calibrate theoretical models…