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ORCIDDavid W. Hogg
Person information
- affiliation: Flatiron Institute, New York, NY, USA
- affiliation (PhD 1998): California Institute of Technology, Pasadena, CA, USA
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Journal Articles
- 2023
[j7]Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu:
Dimensionless machine learning: Imposing exact units equivariance. J. Mach. Learn. Res. 24: 109:1-109:32 (2023)- 2022
- [j6]Ningyuan Teresa Huang, David W. Hogg, Soledad Villar:
Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions. SIAM J. Math. Data Sci. 4(1): 126-152 (2022) - 2020
- [j5]Daniel Hey
, Simon Murphy, Daniel Foreman-Mackey, Timothy Bedding, Benjamin J. S. Pope, David W. Hogg:
Maelstrom: A Python package for identifying companions to pulsating stars from their light travel time variations. J. Open Source Softw. 5(51): 2125 (2020) - 2019
- [j4]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:
emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC. J. Open Source Softw. 4(43): 1864 (2019) - 2016
- [j3]Sivaram Ambikasaran, Daniel Foreman-Mackey, Leslie Greengard, David W. Hogg, Michael O'Neil:
Fast Direct Methods for Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 38(2): 252-265 (2016) - [j2]Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters:
Modeling confounding by half-sibling regression. Proc. Natl. Acad. Sci. USA 113(27): 7391-7398 (2016) - 2014
- [j1]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:
Ten Simple Rules for the Care and Feeding of Scientific Data. PLoS Comput. Biol. 10(4) (2014)
Conference and Workshop Papers
- 2021
- [c3]Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith:
Scalars are universal: Equivariant machine learning, structured like classical physics. NeurIPS 2021: 28848-28863 - 2015
- [c2]Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters:
Removing systematic errors for exoplanet search via latent causes. ICML 2015: 2218-2226 - 2014
- [c1]Dustin Lang, David W. Hogg, Bernhard Schölkopf:
Towards building a Crowd-Sourced Sky Map. AISTATS 2014: 549-557
Informal and Other Publications
- 2024
- [i14]David W. Hogg, Soledad Villar:
Is machine learning good or bad for the natural sciences? CoRR abs/2405.18095 (2024) - [i13]Valentina Tardugno Poleo, Nora Eisner, David W. Hogg:
NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning. CoRR abs/2405.18278 (2024) - 2023
- [i12]Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf:
The passive symmetries of machine learning. CoRR abs/2301.13724 (2023) - [i11]Wilson Gregory, David W. Hogg, Ben Blum-Smith, Maria Teresa Arias, Kaze W. K. Wong, Soledad Villar:
GeometricImageNet: Extending convolutional neural networks to vector and tensor images. CoRR abs/2305.12585 (2023) - 2022
- [i10]Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu:
Dimensionless machine learning: Imposing exact units equivariance. CoRR abs/2204.00887 (2022) - 2021
- [i9]David W. Hogg, Soledad Villar:
Fitting very flexible models: Linear regression with large numbers of parameters. CoRR abs/2101.07256 (2021) - [i8]Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith:
Scalars are universal: Gauge-equivariant machine learning, structured like classical physics. CoRR abs/2106.06610 (2021) - [i7]Weichi Yao, Kate Storey-Fisher, David W. Hogg, Soledad Villar:
A simple equivariant machine learning method for dynamics based on scalars. CoRR abs/2110.03761 (2021) - 2020
- [i6]Ningyuan Huang, David W. Hogg, Soledad Villar:
Dimensionality reduction, regularization, and generalization in overparameterized regressions. CoRR abs/2011.11477 (2020) - 2017
- [i5]Daniela Huppenkothen, Anthony Arendt, David W. Hogg, Karthik Ram, Jake VanderPlas, Ariel Rokem:
Hack Weeks as a model for Data Science Education and Collaboration. CoRR abs/1711.00028 (2017) - 2015
- [i4]Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters:
Removing systematic errors for exoplanet search via latent causes. CoRR abs/1505.03036 (2015) - 2014
- [i3]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:
10 Simple Rules for the Care and Feeding of Scientific Data. CoRR abs/1401.2134 (2014) - [i2]Dustin Lang, David W. Hogg, Bernhard Schölkopf:
Towards building a Crowd-Sourced Sky Map. CoRR abs/1406.1528 (2014) - 2008
- [i1]David W. Hogg, Dustin Lang:
Astronomical imaging: The theory of everything. CoRR abs/0810.3851 (2008)
Coauthor Index
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