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Angelis,\ E.\ ; Quinzan,\ F.*\ ; Soleymani,\ A.*\ ; Jail,\ P.J.*\ ; Bauer,\ S.
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Double machine learning based structure identification from temporal data.
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Trans. Machine Learn. Res. 2025, accepted (2025)
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Manduchi,\ L.*\ ; Meister,\ C.*\ ; Pandey,\ K.C.*\ ; Bamler,\ R.*\ ; Cotterell,\ R.*\ ; Däubener,\ S.*\ ; Fellenz,\ S.*\ ; Fischer,\ A.*\ ; Gärtner,\ T.*\ ; Kirchler,\ M.*\ ; Kloft,\ M.*\ ; Li,\ Y.*\ ; Lippert,\ C.*\ ; de Melo,\ G.*\ ; Nalisnick,\ E.*\ ; Ommer,\ B.*\ ; Ranganath,\ R.*\ ; Waldron,\ M.*\ ; Ullrich,\ K.*\ ; Van den Broeck,\ G.*\ ; Vogt,\ J.E.*\ ; Wang,\ Y.*\ ; Wenzel,\ F.*\ ; Wood,\ F.*\ ; Mandt,\ S.*\ ; Fortuin,\ V.
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On the challenges and opportunities in generative AI.
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Trans. Machine Learn. Res. 2025, accepted (2025)
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Mueller,\ T.T.*\ ; Starck,\ S.*\ ; Bintsi,\ K.M.*\ ; Ziller,\ A.*\ ; Braren,\ R.*\ ; Kaissis,\ G.\ ; Rueckert,\ D.*
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Are Population Graphs Really as Powerful as Believed?
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Trans. Machine Learn. Res. 2024, accepted (2024)
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Nasirigerdeh,\ R.*\ ; Torkzadehmahani,\ R.*\ ; Rueckert,\ D.*\ ; Kaissis,\ G.
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Kernel normalized convolutional networks.
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Trans. Machine Learn. Res. 2024, 107-118 (2024)
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Quinzan,\ F.*\ ; Casolo,\ C.\ ; Muandet,\ K.*\ ; Luo,\ Y.*\ ; Kilbertus,\ N.
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Learning counterfactually invariant predictors.
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Trans. Machine Learn. Res. 2024, accepted (2024)
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Sharma,\ M.*\ ; Rainforth,\ T.*\ ; Teh,\ Y.W.*\ ; Fortuin,\ V.
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Incorporating unlabelled data into bayesian neural networks.
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Trans. Machine Learn. Res. 2024, accepted (2024)
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von Rohrscheidt,\ J.C.\ ; Rieck,\ B.\ ; Schmon,\ S.M.*
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Bayesian computation meets topology.
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Trans. Machine Learn. Res. 2024, accepted (2024)
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