David W. Hogg

Orcid: 0000-0003-2866-9403

Affiliations:
  • Flatiron Institute, New York, NY, USA
  • California Institute of Technology, Pasadena, CA, USA (PhD 1998)


According to our database1, David W. Hogg authored at least 22 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
Towards fully covariant machine learning.
Trans. Mach. Learn. Res., 2024

NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning.
CoRR, 2024

Is machine learning good or bad for the natural sciences?
CoRR, 2024

Position: Is machine learning good or bad for the natural sciences?
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Dimensionless machine learning: Imposing exact units equivariance.
J. Mach. Learn. Res., 2023

GeometricImageNet: Extending convolutional neural networks to vector and tensor images.
CoRR, 2023

The passive symmetries of machine learning.
CoRR, 2023

2022
Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions.
SIAM J. Math. Data Sci., 2022

2021
A simple equivariant machine learning method for dynamics based on scalars.
CoRR, 2021

Scalars are universal: Gauge-equivariant machine learning, structured like classical physics.
CoRR, 2021

Fitting very flexible models: Linear regression with large numbers of parameters.
CoRR, 2021

Scalars are universal: Equivariant machine learning, structured like classical physics.
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.
J. Open Source Softw., 2020

2019
emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC.
J. Open Source Softw., 2019

2017
Hack Weeks as a model for Data Science Education and Collaboration.
CoRR, 2017

2016
Modeling confounding by half-sibling regression.
Proc. Natl. Acad. Sci. USA, 2016

Fast Direct Methods for Gaussian Processes.
IEEE Trans. Pattern Anal. Mach. Intell., 2016

2015
Removing systematic errors for exoplanet search via latent causes.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Ten Simple Rules for the Care and Feeding of Scientific Data.
PLoS Comput. Biol., 2014

10 Simple Rules for the Care and Feeding of Scientific Data.
CoRR, 2014

Towards building a Crowd-Sourced Sky Map.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2008
Astronomical imaging: The theory of everything
CoRR, 2008


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