Jacob VanderPlas

According to our database, Jacob VanderPlas authored at least 20 papers between 2011 and 2020.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2020

Applying Information Theory to Design Optimal Filters for Photometric Redshifts.

J. Bryce Kalmbach, Jacob VanderPlas, Andrew J. Connolly

CoRR, 2020

2019

SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python.

Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, Ilhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, SciPy 1. 0 Contributors

CoRR, 2019

2018

Altair: Declarative statistical visualization library for Python.

Jacob VanderPlas, Brian E. Granger, Jeffrey Heer, Dominik Moritz, Kanit Wongsuphasawat, Arvind Satyanarayan, Eitan Lees, Ilia Timofeev, Ben Welsh, Scott Sievert

Dataset, November, 2018

Altair: Declarative statistical visualization library for Python.

Jacob VanderPlas, Brian E. Granger, Jeffrey Heer, Dominik Moritz, Kanit Wongsuphasawat, Eitan Lees, Ilia Timofeev, Ben Welsh, Scott Sievert

Dataset, November, 2018

Journal of Open Source Software (JOSS): design and first-year review.

Arfon M. Smith, Kyle E. Niemeyer, Daniel S. Katz, Lorena A. Barba, George Githinji, Melissa Gymrek, Kathryn D. Huff, Christopher R. Madan, Abigail Cabunoc Mayes, Kevin M. Moerman, Pjotr Prins, Karthik Ram, Ariel Rokem, Tracy K. Teal, Roman Valls Guimera, Jacob VanderPlas

PeerJ Comput. Sci., 2018

Altair: Interactive Statistical Visualizations for Python.

Jacob VanderPlas, Brian E. Granger, Jeffrey Heer, Dominik Moritz, Kanit Wongsuphasawat, Arvind Satyanarayan, Eitan Lees, Ilia Timofeev, Ben Welsh, Scott Sievert

J. Open Source Softw., 2018

approxposterior: Approximate Posterior Distributions in Python.

David P. Fleming, Jake VanderPlas

J. Open Source Softw., 2018

From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning.

Juan B. Cabral, Bruno Sánchez, Felipe Ramos, Sebastián Gurovich, Pablo M. Granitto, Jacob VanderPlas

Astron. Comput., 2018

2017

Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads.

Parmita Mehta, Sven Dorkenwald, Dongfang Zhao, Tomer Kaftan, Alvin Cheung, Magdalena Balazinska, Ariel Rokem, Andrew J. Connolly, Jacob VanderPlas, Yusra AlSayyad

Proc. VLDB Endow., 2017

Hack Weeks as a model for Data Science Education and Collaboration.

Daniela Huppenkothen, Anthony Arendt, David W. Hogg, Karthik Ram, Jake VanderPlas, Ariel Rokem

CoRR, 2017

2016

mst_clustering: Clustering via Euclidean Minimum Spanning Trees.

Jake VanderPlas

J. Open Source Softw., 2016

Megaman: Scalable Manifold Learning in Python.

James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang

J. Mach. Learn. Res., 2016

megaman: Manifold Learning with Millions of points.

James McQueen, Marina Meila, Jacob VanderPlas, Zhongyue Zhang

CoRR, 2016

2014

Frequentism and Bayesianism: A Python-driven Primer.

Jake VanderPlas

Proceedings of the 13th Python in Science Conference, 2014

2013

A Demonstration of Iterative Parallel Array Processing in Support of Telescope Image Analysis.

Matthew I. Moyers, Emad Soroush, Spencer Wallace, K. Simon Krughoff, Jake VanderPlas, Magdalena Balazinska, Andrew J. Connolly

Proc. VLDB Endow., 2013

Squeezing a Big Orange into Little Boxes: The AscotDB System for Parallel Processing of Data on a Sphere.

Jacob VanderPlas, Emad Soroush, K. Simon Krughoff, Magdalena Balazinska

IEEE Data Eng. Bull., 2013

API design for machine learning software: experiences from the scikit-learn project.

Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, Gaël Varoquaux

CoRR, 2013

2012

Introduction to astroML: Machine learning for astrophysics.

Jacob VanderPlas, Andrew J. Connolly, Zeljko Ivezic, Alexander G. Gray

Proceedings of the 2012 Conference on Intelligent Data Understanding, 2012

2011

Hierarchical Probabilistic Models for Group Anomaly Detection.

Liang Xiong, Barnabás Póczos, Jeff G. Schneider, Andrew J. Connolly, Jake VanderPlas

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Scikit-learn: Machine Learning in Python.

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Edouard Duchesnay

J. Mach. Learn. Res., 2011