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- Institut national de recherche en sciences et technologies du numériqueFR143 papers
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19 papers · 1 filter
Topological Learning for Motion Data via Mixed Coordinates
Hengrui Luo, Jisu Kim, Alice Patania +1
Topology can extract the structural information in a dataset efficiently. In this paper, we attempt to incorporate topological information into a multiple output Gaussian process m…
Statistically Valid Variable Importance Assessment through Conditional Permutations
Ahmad Chamma, Denis A. Engemann, Bertrand Thirion
Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-ba…
A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification
Pierre Colombo, Nathan Noiry, Guillaume Staerman +1
One of the pursued objectives of deep learning is to provide tools that learn abstract representations of reality from the observation of multiple contextual situations. More preci…
Dissecting Causal Biases
Rūta Binkytė, Sami Zhioua, Yassine Turki
Accurately measuring discrimination in machine learning-based automated decision systems is required to address the vital issue of fairness between subpopulations and/or individual…
Modularity in Deep Learning: A Survey
Haozhe Sun, Isabelle Guyon
Modularity is a general principle present in many fields. It offers attractive advantages, including, among others, ease of conceptualization, interpretability, scalability, module…
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
Filippo Galli, Kangsoo Jung, Sayan Biswas +2
Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their…