AO

Showing results 1 to 12 of 580 in total

AO : 1 to 12 of 580 in total

  • Pré-publication, Document de travail

    Armand Lacombe, Michèle Sébag. Asymmetrical Latent Representation for Individual Treatment Effect Modeling. 2026. ⟨hal-05490226⟩

    AO

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  • Thèse

    Nilo Schwencke. Natural gradients and kernel methods for Physics Informed Neural Networks (PINNs). Machine Learning [cs.LG]. Université paris saclay, 2025. English. ⟨NNT : 2025UPASG087⟩. ⟨tel-05474926v1⟩

    AO

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  • Thèse

    Jean-Baptiste Malagnoux. Apprentissage de dictionnaires convolutifs et factorisation non négative de matrices pour la séparation de sources et les problèmes inverses. Mathématiques [math]. Université Paris - Saclay, 2025. Français. ⟨NNT : 2025UPASG096⟩. ⟨tel-05472451⟩

    AO

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  • Thèse

    Alice Lacan. Transcriptomics data generation with deep generative models. Artificial Intelligence [cs.AI]. Université Paris-Saclay, 2025. English. ⟨NNT : 2025UPASG010⟩. ⟨tel-04996930⟩

    AO

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  • Thèse

    Antoine Szatkownik. Latent generative modeling and synthetic data evaluation in population genomics. Computer Science [cs]. Université Paris-Saclay, 2025. English. ⟨NNT : 2020UPASA001⟩. ⟨tel-05475569⟩

    AO, BioInfo

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  • Communication dans un congrès

    Burak Yelmen, Merve Nur Güler, Tõnu Kollo, Märt Möls, Guillaume Charpiat, et al.. Bias in genome-wide association test statistics due to omitted interactions. RECOMB 2026 - 30th Annual International Conference on Research in Computational Molecular Biology, May 2026, Thessaloniki, Greece. ⟨10.1101/2025.11.21.689603⟩. ⟨hal-05474710⟩

    AO, BioInfo

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  • Article dans une revue

    Michael Tangermann, Sylvain Chevallier, Matthias Dold, Pierre Guetschel, Reinmar Kobler, et al.. Learning from small datasets -- review of workshop 6 of the 10th International BCI Meeting 2023. Journal of Neural Engineering, 2025, 22 (3), pp.033001. ⟨10.1088/1741-2552/addf80⟩. ⟨hal-05462858⟩

    AO

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  • Pré-publication, Document de travail

    Anaclara Alvez-Canepa, Cyril Furtlehner, François Landes. Learning and extrapolating scale-invariant processes. 2025. ⟨hal-05470825⟩

    AO

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  • Communication dans un congrès

    Giovanni Catania, Aurélien Decelle, Cyril Furtlehner, Beatriz Seoane. A theoretical framework for overfitting in energy-based modeling. ICML 2025 - Forty-Second International Conference on Machine Learning, Jul 2025, Vancouver, Canada. pp.6891-6919. ⟨hal-05466288⟩

    AO

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  • Pré-publication, Document de travail

    Sergio Chibbaro, Cyril Furtlehner, Théo Marchetta, Andrei-Tiberiu Pantea, Davide Rossetti. Building causation links in stochastic nonlinear systems from data. 2025. ⟨hal-05466316⟩

    AO

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  • Thèse

    Audrey Poinsot. Structural Causal Models for Synthetic Data Generation. Other [cs.OH]. Université Paris-Saclay, 2025. English. ⟨NNT : 2025UPASG097⟩. ⟨tel-05461449⟩

    AO

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  • Pré-publication, Document de travail

    Panayiotis Panayiotou, Audrey Poinsot, Alessandro Leite, Nicolas Chesneau, Marc Schoenauer, et al.. CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning. 2026. ⟨hal-05455135⟩

    AO

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