Leonard Sasse

According to our database, Leonard Sasse authored at least 4 papers between 2023 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Empirical Comparison Between Cross-Validation and Mutation-Validation in Model Selection.

Jinyang Yu, Sami Hamdan, Leonard Sasse, Abigail Morrison, Kaustubh R. Patil

Proceedings of the Advances in Intelligent Data Analysis XXII, 2024

2023

nilearn.

Nilearn Contributors, Ahmad Chamma, Aina Frau-Pascual, Alex Rothberg, Alexandre Abadie, Alexandre Abraham, Alexandre Gramfort, Alexandre Savio, Alexandre Cionca, Alexis Thual, Alisha Kodibagkar, Amadeus Kanaan, Ana Luísa Pinho, Andrés Hoyos Idrobo, Anne-Sophie Kieslinger, Ariel Rokem, Arthur Mensch, Aswin Vijayan, Audrey Duran, Ben Cipollini, Bertrand Thirion, Binh Nguyen, Caglar Cakan, Chris Gorgolewski, Chris Markiewicz, Christian Horea, Christian Gerloff, Colin Reininger, Connor Lane, Czarina Sy, Céline Delettre, Dan Gale, Daniel Gomez, Danilo Bzdok, David G. Ellis, Demian Wassermann, Derek Pisner, Dimitri Papadopoulos Orfanos, Elizabeth DuPre, Elvis Dohmatob, Eric Larson, Evan Edmond, Fabian Pedregosa, Franz Liem, François Paugam, Gaël Varoquaux, Gilles de Hollander, Gregory Kiar, Greydon Gilmore, Guillaume Lemaitre, Hao-Ting Wang, Himanshu Aggarwal, Ian Abenes, Jake Vogel, Ján Margeta, Jaques Grobler, Jason Gors, Javier Rasero, Jean Kossaifi, Jean-Rémi King, Jelle Roelof Dalenberg, Jeremy Lefort-Besnard, Jérôme Dockès, Jérôme-Alexis Chevalier, Johannes Wiesner, Jon Haitz Legarreta Gorrono, Jona Sassenhagen, Joshua Teves, Julia M. Huntenburg, Julio A. Peraza-Goicolea, Kamalakar Reddy Daddy, Kevin R. Sitek, Koen Helwegen, Konrad Wagstyl, Konstantin Shmelkov, Kshitij Chawla, Kun Chen, Leonard Sasse, Loic Estève, Loïc Tetrel, Luz Paz, Manon Pietrantoni, Martin Perez-Guevara, Martin Wegrzyn, Mathias Goncalves, Mathieu Dugré, Matthias Ekman, Matthieu Joulot, Maximilian Cosmo Sitter, Mehdi Rahim, Michael Eickenberg, Michael Hanke, Michael Notter, Michael Waskom, Michelle Wang, Mohammad Torabi, Moritz Boos, Myeong Seop Song, Natasha Clarke, Neelay Shah, Nicolas Gensollen, Oliver Warrington, Oscar Esteban, Patrick S. Sadil, Paul Bogdan, Paula Sanz-Leon, Peer Herholz, Philippe Gervais, Pierre Bellec, Pierre Glaser, Pierre-Olivier Quirion, Pradeep Reddy Raamana, Rahul Brito, Raphaël Meudec, Robert Luke, Robert Williamson, Roberto Guidotti, Ronald Phlypo, Ryan Hammonds, Rémi Gau, Sachin Patalasingh, Sage Hahn, Salma Bougacha, Sam Buck Johnson, Sami Jawhar, Simon Steinkamp, Sourav Singh, Steven L. Meisler, Sylvain Lan, Sylvain Takerkart, Tarun Samanta, Taylor Salo, Thomas Bazeille, Tom Vanasse, Vasco Diogo, Vincent Michel, Virgile Fritsch, Yaroslav O. Halchenko, Yasmin Mzayek, Zvi Baratz, Óscar Nájera

Dataset, October, 2023

On Leakage in Machine Learning Pipelines.

Leonard Sasse, Eliana Nicolaisen-Sobesky, Juergen Dukart, Simon B. Eickhoff, Michael Götz, Sami Hamdan, Vera Komeyer, Abhijit Kulkarni, Juha Lahnakoski, Bradley C. Love, Federico Raimondo, Kaustubh R. Patil

CoRR, 2023

Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models.

Sami Hamdan, Shammi More, Leonard Sasse, Vera Komeyer, Kaustubh R. Patil, Federico Raimondo

CoRR, 2023