Kaustubh R. Patil

Orcid: 0000-0002-0289-5480

Affiliations:

Heinrich Heine University Düsseldorf, Germany
Research Centre Jülich, Germany
Massachusetts Institute of Technology, Sloan Neuroeconomics Lab, Cambridge, MA, USA (former)
Max Planck Institute for Informatics, Saarbrücken, Germany (former)
Saarland University, Saarbrücken, Germany (PhD 2013)
University of Porto, Portugal

According to our database¹, Kaustubh R. Patil authored at least 25 papers between 2008 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Large language models surpass human experts in predicting neuroscience results.

[BibT_eX]

[DOI]

CoRR, 2024

2023

A systematic comparison of VBM pipelines and their application to age prediction.

[BibT_eX]

[DOI]

Georgios Antonopoulos

NeuroImage, October, 2023

A topography-based predictive framework for naturalistic viewing fMRI.

[BibT_eX]

[DOI]

NeuroImage, August, 2023

Naturalistic viewing increases individual identifiability based on connectivity within functional brain networks.

[BibT_eX]

[DOI]

NeuroImage, June, 2023

Brain-age prediction: A systematic comparison of machine learning workflows.

[BibT_eX]

[DOI]

Shammi More

Georgios Antonopoulos

NeuroImage, April, 2023

A too-good-to-be-true prior to reduce shortcut reliance.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., February, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

On Leakage in Machine Learning Pipelines.

[BibT_eX]

[DOI]

Leonard Sasse

Eliana Nicolaisen-Sobesky

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

2022

Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.

[BibT_eX]

[DOI]

Kaustubh Raosaheb Patil

Athanasios Typas

Kiran Raosaheb Patil

PLoS Comput. Biol., 2022

Confound-leakage: Confound Removal in Machine Learning Leads to Leakage.

[BibT_eX]

[DOI]

CoRR, 2022

Predictive Data Calibration for Linear Correlation Significance Testing.

[BibT_eX]

[DOI]

Kaustubh R. Patil

Simon B. Eickhoff

Robert Langner

CoRR, 2022

Smartphone-Based Digital Biomarkers for Parkinson's Disease in a Remotely-Administered Setting.

[BibT_eX]

[DOI]

IEEE Access, 2022

2021

Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate.

[BibT_eX]

[DOI]

NeuroImage, 2021

Imaging evolution of the primate brain: the next frontier?

[BibT_eX]

[DOI]

NeuroImage, 2021

2020

Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020

Evolving complex yet interpretable representations: application to Alzheimer's diagnosis and prognosis.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2020

2019

Rank Selection in Non-negative Matrix Factorization: systematic comparison and a new MAD metric.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2019

2018

Evaluation of non-negative matrix factorization of grey matter in age prediction.

[BibT_eX]

[DOI]

NeuroImage, 2018

A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data.

[BibT_eX]

[DOI]

Guillem Collell

Drazen Prelec

Kaustubh R. Patil

Neurocomputing, 2018

2017

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.

[BibT_eX]

[DOI]

Gagan Joshi

Sheeba Arnold Anteraper