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)

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)

Overview of leakage scenarios in supervised machine learning. - 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 (J. Big Data, 2025)