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20232026
most citedOn the Generalization and Approximation Capacities of Neural Controlled Differential Equations

1 citations · 2 across the 7 of their papers we have counts for

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5 papers · 1 filter

stat.ML2026

Optimal Transport under Group Fairness Constraints

Linus Bleistein, Mathieu Dagréou, Francisco Andrade +2

Ensuring fairness in matching algorithms is a key challenge in allocating scarce resources and positions. Focusing on Optimal Transport (OT), we introduce a novel notion of group f…

stat.ML2025

Optimal Transport with Heterogeneously Missing Data

Linus Bleistein, Aurélien Bellet, Julie Josse

We consider the problem of solving the optimal transport problem between two empirical distributions with missing values. Our main assumption is that the data is missing completely…

stat.ML2024

Dynamical Survival Analysis with Controlled Latent States

Linus Bleistein, Van-Tuan Nguyen, Adeline Fermanian +1

We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series. We introduce a novel mode…

stat.ML2023★ 1 cited

On the Generalization and Approximation Capacities of Neural Controlled Differential Equations

Linus Bleistein, Agathe Guilloux

Neural Controlled Differential Equations (NCDEs) are a state-of-the-art tool for supervised learning with irregularly sampled time series (Kidger, 2020). However, no theoretical an…

stat.ML2023★ 1 cited

Learning the Dynamics of Sparsely Observed Interacting Systems

Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot +1

We address the problem of learning the dynamics of an unknown non-parametric system linking a target and a feature time series. The feature time series is measured on a sparse and…