Kernel-enabled methods for subspace regression and efficient control. - Kaustubh R. Patil; Abhijit Kulkarni (Int. J. Model. Identif. Control., 2008)

Genome signature based sequence comparison for taxonomic assignment and tree inference. - Kaustubh R. Patil (2013)

Optimal Teaching for Limited-Capacity Human Learners. - Kaustubh R. Patil; Xiaojin Zhu 001; Lukasz Kopec; Bradley C. Love (NIPS, 2014)

Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary and Multiclass Imbalanced Data. - Guillem Collell; Drazen Prelec; Kaustubh R. Patil (CoRR, 2016)

Integration and Segregation of Default Mode Network Resting-State Functional Connectivity in Transition-Age Males with High-Functioning Autism Spectrum Disorder: A Proof-of-Concept Study. - Gagan Joshi; Sheeba Arnold Anteraper; Kaustubh R. Patil; Meha Semwal; Rachel L. Goldin; Stephannie L. Furtak; Xiaoqian J. Chai; Zeynep M. Saygin; John D. E. Gabrieli; Joseph Biederman; Susan L. Whitfield-Gabrieli (Brain Connect., 2017)

A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data. - Guillem Collell; Drazen Prelec; Kaustubh R. Patil (Neurocomputing, 2018)

Evaluation of non-negative matrix factorization of grey matter in age prediction. - Deepthi P. Varikuti; Sarah Genon; Aristeidis Sotiras; Holger Schwender; Felix Hoffstaedter; Kaustubh R. Patil; Christiane Jockwitz; Svenja Caspers; Susanne Moebus; Katrin Amunts; Christos Davatzikos; Simon B. Eickhoff (NeuroImage, 2018)

Rank Selection in Non-negative Matrix Factorization: systematic comparison and a new MAD metric. - Laura Muzzarelli; Susanne Weis; Simon B. Eickhoff; Kaustubh R. Patil (IJCNN, 2019)

Evolving complex yet interpretable representations: application to Alzheimer's diagnosis and prognosis. - Jean-Philippe Kröll; Simon B. Eickhoff; Felix Hoffstaedter; Kaustubh R. Patil (CEC, 2020)

Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study. - Shammi More; Simon B. Eickhoff; Julian Caspers; Kaustubh R. Patil (ECML/PKDD (5), 2020)

Imaging evolution of the primate brain: the next frontier? - Patrick Friedrich; Stephanie J. Forkel; Céline Amiez; Joshua H. Balsters; Olivier Coulon; Lingzhong Fan; Alexandros Goulas; Fadila Hadj-Bouziane; Erin E. Hecht; Katja Heuer; Tianzi Jiang; Robert D. Latzman; et al. (NeuroImage, 2021)

Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate. - Xiaojin Liu 002; Simon B. Eickhoff; Svenja Caspers; Jianxiao Wu; Sarah Genon; Felix Hoffstaedter; Rogier B. Mars; Iris E. Sommer; Claudia R. Eickhoff; Ji Chen; Renaud Jardri; Kathrin Reetz; et al. (NeuroImage, 2021)

Smartphone-Based Digital Biomarkers for Parkinson's Disease in a Remotely-Administered Setting. - María Goñi; Simon B. Eickhoff; Mehran Sahandi Far; Kaustubh R. Patil; Juergen Dukart (IEEE Access, 2022)

Predictive Data Calibration for Linear Correlation Significance Testing. - Kaustubh R. Patil; Simon B. Eickhoff; Robert Langner (CoRR, 2022)

Confound-leakage: Confound Removal in Machine Learning Leads to Leakage. - Sami Hamdan; Bradley C. Love; Georg G. von Polier; Susanne Weis; Holger Schwender; Simon B. Eickhoff; Kaustubh R. Patil (CoRR, 2022)

Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs. - Vinita Periwal; Stefan Bassler; Sergej Andrejev; Natalia Gabrielli; Kaustubh Raosaheb Patil; Athanasios Typas; Kiran Raosaheb Patil (PLoS Comput. Biol., 2022)

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)

A too-good-to-be-true prior to reduce shortcut reliance. - Nikolay Dagaev; Brett D. Roads; Xiaoliang Luo; Daniel N. Barry; Kaustubh R. Patil; Bradley C. Love (Pattern Recognit. Lett., 2023)

Brain-age prediction: A systematic comparison of machine learning workflows. - Shammi More; Georgios Antonopoulos; Felix Hoffstaedter; Julian Caspers; Simon B. Eickhoff; Kaustubh R. Patil (NeuroImage, 2023)

Naturalistic viewing increases individual identifiability based on connectivity within functional brain networks. - Jean-Philippe Kröll; Patrick Friedrich; Xuan Li; Kaustubh R. Patil; Lisa Mochalski; Laura K. Waite; Xing Qian; Michael W. L. Chee; Juan Helen Zhou; Simon B. Eickhoff; Susanne Weis (NeuroImage, 2023)

A topography-based predictive framework for naturalistic viewing fMRI. - Xuan Li; Patrick Friedrich; Kaustubh R. Patil; Simon B. Eickhoff; Susanne Weis (NeuroImage, 2023)

A systematic comparison of VBM pipelines and their application to age prediction. - Georgios Antonopoulos; Shammi More; Federico Raimondo; Simon B. Eickhoff; Felix Hoffstaedter; Kaustubh R. Patil (NeuroImage, 2023)

Empirical Comparison Between Cross-Validation and Mutation-Validation in Model Selection. - Jinyang Yu; Sami Hamdan; Leonard Sasse; Abigail Morrison; Kaustubh R. Patil (IDA (2), 2024)

FastGPR: Divide-and-Conquer Technique in Neuroimaging Data Shortens Training Time and Improves Accuracy. - Federico Raimondo; Georgios Antonopoulos; Simon B. Eickhoff; Kaustubh R. Patil (ISBI, 2024)

Large language models surpass human experts in predicting neuroscience results. - Xiaoliang Luo; Akilles Rechardt; Guangzhi Sun; Kevin K. Nejad; Felipe Yáñez; Bati Yilmaz; Kangjoo Lee; Alexandra O. Cohen; Valentina Borghesani; Anton Pashkov; Daniele Marinazzo; Jonathan Nicholas; et al. (CoRR, 2024)

Impact of Leakage on Data Harmonization in Machine Learning Pipelines in Class Imbalance Across Sites. - Nicolás Nieto; Simon B. Eickhoff; Christian Jung 002; Martin Reuter 001; Kersten Diers; Malte Kelm; Artur Lichtenberg; Federico Raimondo; Kaustubh R. Patil (CoRR, 2024)

The impact of MRI image quality on statistical and predictive analysis on voxel based morphology. - Felix Hoffstaedter; Nicolás Nieto; Simon B. Eickhoff; Kaustubh R. Patil (CoRR, 2024)

Region-wise stacking ensembles for estimating brain-age using MRI. - Georgios Antonopoulos; Shammi More; Simon B. Eickhoff; Federico Raimondo; Kaustubh R. Patil (CoRR, 2025)

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)