Leave-one-out cross-validation is risk consistent for lasso von Homrighausen, Darren, McDonald, Daniel J.

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Semi-supervised learning for photometric supernova classification von Richards, Joseph W., Homrighausen, Darren, Freeman, Peter E., Schafer, Chad M., Poznanski, Dovi

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RISK CONSISTENCY OF CROSS-VALIDATION WITH LASSO-TYPE PROCEDURES von Homrighausen, Darren, McDonald, Daniel J.

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Comparison of discharge pulses in temperate and tropical rainforest headwater stream networks von Lininger, Katherine B., Raimondi, Justin, Kramer, Natalie, Homrighausen, Darren, Covich, Alan

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Leave-one-out cross-validation is risk consistent for lasso: Special Issue of the ECML/PKDD 2014 Journal Track von HOMRIGHAUSEN, Darren, MCDONALD, Daniel J

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On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Datasets von Homrighausen, Darren, McDonald, Daniel J.

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Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface composition von STEPHEN, Kate E, HOMRIGHAUSEN, Darren, DEPALMA, Glen, NAKATSU, Cindy H, IRUDAYARAJ, Joseph

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Semi-supervised learning for photometric supernova classification: Semi-supervised SN classification von Richards, Joseph W., Homrighausen, Darren, Freeman, Peter E., Schafer, Chad M., Poznanski, Dovi

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Surface enhanced Raman spectroscopy (SERS) for the discrimination of Arthrobacter strains based on variations in cell surface compositionElectronic supplementary information (ESI)... von Stephen, Kate E, Homrighausen, Darren, DePalma, Glen, Nakatsu, Cindy H, Irudayaraj, Joseph

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Computationally efficient estimators for sequential and resolution-limited inverse problems von Homrighausen, Darren, Genovese, Christopher R.

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A study on tuning parameter selection for the high-dimensional lasso von Homrighausen, Darren, McDonald, Daniel J

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Compressed and Penalized Linear Regression von Homrighausen, Darren, McDonald, Daniel J.

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Compressed and Penalized Linear Regression von Homrighausen, Darren, McDonald, Daniel J

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On the Nystr\"om and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets von Homrighausen, Darren, McDonald, Daniel J

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Risk-consistency of cross-validation with lasso-type procedures von Homrighausen, Darren, McDonald, Daniel J

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Efficient Estimators for Sequential and Resolution-Limited Inverse Problems von Homrighausen, Darren, Genovese, Christopher R

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Leave-one-out cross-validation is risk consistent for lasso von Homrighausen, Darren, McDonald, Daniel J

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Risk-consistency of cross-validation with lasso-type procedures von Homrighausen, Darren, McDonald, Daniel J

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On the Nyström and Column-Sampling Methods for the Approximate Principal Components Analysis of Large Data Sets von Homrighausen, Darren, McDonald, Daniel J

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Spectral approximations in machine learning von Homrighausen, Darren, McDonald, Daniel J

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