David W. Hogg

According to our database, David W. Hogg authored at least 22 papers between 2008 and 2024.

Collaborative distances:

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

2024

Towards fully covariant machine learning.

Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf

Trans. Mach. Learn. Res., 2024

NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning.

Valentina Tardugno Poleo, Nora Eisner, David W. Hogg

CoRR, 2024

Is machine learning good or bad for the natural sciences?

David W. Hogg, Soledad Villar

CoRR, 2024

Position: Is machine learning good or bad for the natural sciences?

David W. Hogg, Soledad Villar

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Dimensionless machine learning: Imposing exact units equivariance.

Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu

J. Mach. Learn. Res., 2023

GeometricImageNet: Extending convolutional neural networks to vector and tensor images.

Wilson Gregory, David W. Hogg, Ben Blum-Smith, Maria Teresa Arias, Kaze W. K. Wong, Soledad Villar

CoRR, 2023

The passive symmetries of machine learning.

Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf

CoRR, 2023

2022

Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions.

Ningyuan Teresa Huang, David W. Hogg, Soledad Villar

SIAM J. Math. Data Sci., 2022

2021

A simple equivariant machine learning method for dynamics based on scalars.

Weichi Yao, Kate Storey-Fisher, David W. Hogg, Soledad Villar

CoRR, 2021

Scalars are universal: Gauge-equivariant machine learning, structured like classical physics.

Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith

CoRR, 2021

Fitting very flexible models: Linear regression with large numbers of parameters.

David W. Hogg, Soledad Villar

CoRR, 2021

Scalars are universal: Equivariant machine learning, structured like classical physics.

Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Maelstrom: A Python package for identifying companions to pulsating stars from their light travel time variations.

Daniel Hey, Simon Murphy, Daniel Foreman-Mackey, Timothy Bedding, Benjamin J. S. Pope, David W. Hogg

J. Open Source Softw., 2020

2019

emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC.

Daniel Foreman-Mackey, Will Farr, Manodeep Sinha, Anne M. Archibald, David W. Hogg, Jeremy Sanders, Joe Zuntz, Peter H. Williams, Andrew R. J. Nelson, Miguel de Val-Borro, Tobias Erhardt, Ilya Pashchenko, Oriol Abril Pla

J. Open Source Softw., 2019

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

Modeling confounding by half-sibling regression.

Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters

Proc. Natl. Acad. Sci. USA, 2016

Fast Direct Methods for Gaussian Processes.

Sivaram Ambikasaran, Daniel Foreman-Mackey, Leslie Greengard, David W. Hogg, Michael O'Neil

IEEE Trans. Pattern Anal. Mach. Intell., 2016

2015

Removing systematic errors for exoplanet search via latent causes.

Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Ten Simple Rules for the Care and Feeding of Scientific Data.

Alyssa Goodman, Alberto Pepe, Alexander W. Blocker, Christine L. Borgman, Kyle Cranmer, Mercè Crosas, Rosanne Di Stefano, Yolanda Gil, Paul Groth, Margaret L. Hedstrom, David W. Hogg, Vinay Kashyap, Ashish Mahabal, Aneta Siemiginowska, Aleksandra B. Slavkovic

PLoS Comput. Biol., 2014

10 Simple Rules for the Care and Feeding of Scientific Data.

Alyssa Goodman, Alberto Pepe, Alexander W. Blocker, Christine L. Borgman, Kyle Cranmer, Mercè Crosas, Rosanne Di Stefano, Yolanda Gil, Paul Groth, Margaret L. Hedstrom, David W. Hogg, Vinay Kashyap, Ashish Mahabal, Aneta Siemiginowska, Aleksandra B. Slavkovic

CoRR, 2014

Towards building a Crowd-Sourced Sky Map.

Dustin Lang, David W. Hogg, Bernhard Schölkopf

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2008

Astronomical imaging: The theory of everything

David W. Hogg, Dustin Lang

CoRR, 2008