| Semi-supervised learning for photometric supernova classification JW Richards, D Homrighausen, PE Freeman, CM Schafer, D Poznanski Monthly Notices of the Royal Astronomical Society 419 (2), 1121-1135, 2012 | 76 | 2012 |
| The lasso, persistence, and cross-validation D Homrighausen, D McDonald International conference on machine learning, 1031-1039, 2013 | 49 | 2013 |
| Leave-one-out cross-validation is risk consistent for lasso D Homrighausen, DJ McDonald Machine learning 97 (1), 65-78, 2014 | 47 | 2014 |
| Leave-one-out cross-validation is risk consistent for lasso D Homrighausen, DJ McDonald Machine learning 97 (1), 65-78, 2014 | 47 | 2014 |
| Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition KE Stephen, D Homrighausen, G DePalma, CH Nakatsu, J Irudayaraj Analyst 137 (18), 4280-4286, 2012 | 44 | 2012 |
| Regularization techniques for PSF-matching kernels-I. Choice of kernel basis AC Becker, D Homrighausen, AJ Connolly, CR Genovese, R Owen, ... Monthly Notices of the Royal Astronomical Society 425 (2), 1341-1349, 2012 | 38 | 2012 |
| Risk consistency of cross-validation with lasso-type procedures D Homrighausen, DJ McDonald Statistica Sinica, 1017-1036, 2017 | 33 | 2017 |
| On the Nyström and column-sampling methods for the approximate principal components analysis of large datasets D Homrighausen, DJ McDonald Journal of Computational and Graphical Statistics 25 (2), 344-362, 2016 | 17 | 2016 |
| A study on tuning parameter selection for the high-dimensional lasso D Homrighausen, DJ McDonald Journal of Statistical Computation and Simulation 88 (15), 2865-2892, 2018 | 12* | 2018 |
| Image Co-Addition with Temporally Varying Kernels D Homrighausen, CR Genovese, AJ Connolly, AC Becker, R Owen Publications of the Astronomical Society of the Pacific 123 (907), 1117, 2011 | 12 | 2011 |
| Spectral approximations in machine learning D Homrighausen, DJ McDonald arXiv preprint arXiv:1107.4340, 2011 | 7 | 2011 |
| Compressed and penalized linear regression D Homrighausen, DJ McDonald Journal of Computational and Graphical Statistics 29 (2), 309-322, 2020 | 5 | 2020 |
| Computationally efficient estimators for sequential and resolution-limited inverse problems D Homrighausen, CR Genovese | | 2013 |
| Efficient Estimators for Sequential and Resolution-Limited Inverse Problems D Homrighausen, CR Genovese arXiv preprint arXiv:1207.0538, 2012 | | 2012 |
| A BAYESIAN APPROACH TO PREDICTING RECESSIONS 36-724 PRELIMINARY REPORT D PERCIVAL, D MCDONALD, D HOMRIGHAUSEN, ... | | |
