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 Author  Title  Journal
 2012 Richards, et al. Semi-supervised learning for photometric supernova classification (arXiv) MNRAS accepted
 2011 Morgan, et al.Rapid, Machine-Learned Resource Allocation: Application to High-redshift GRB Follow-up (arXiv) ApJ, accepted
 2011 Starr, et al.ALLStars: Overcoming Multi-Survey Selection Bias using Crowd-Sourced Active Learning (pdf) ADASS 2011 proceedings
 2011 Richards, et al.Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification (arXiv v2) ApJ
 2011 Bloom, et al. Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era (arXiv) PASP
 2011 Klein, et al.Mid-infrared Period-Luminosity Relations of RR Lyrae Stars Derived from the WISE Preliminary Data Release (arXiv) ApJ
 2011     Bloom & RichardsData Mining and Machine-Learning in Time-Domain Discovery & Classification, in "Advances in Machine Learning and Data Mining for Astronomy" (arXiv) Chapter in Book
 2011 Richards, et al. Semi-supervised Learning for Photometric Supernova Classification (arXiv) MNRAS
 2011 Richards, et al.On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data (arXiv v1)ApJ
 2011 BloomWhat Are Gamma-Ray Bursts? (www)Book: Princeton Frontiers in Physics
 2011 Butler & BloomOptimal Time-series Selection of Quasars  (ADS) AJ
 Brewer & Bloom
Timeseries in VOEvents  (chapter)
 HTU-2 Book
 Jan 20, 2010
 Starr, et al.
A Map/Reduce Parallelized Framework for Rapidly Classifying Astrophysical Transients (pdf). 
 ADASS 2009 proceedings
 Jan 26, 2009
 Starr, et al.
 TCP Classification paper for ADASS 2008 Conference proceedings publication (pdf)
 ADASS 2008 proceedings
Oct 31, 2008
 Starr, et al.
 TCP paper in SciPy2008 CalTech conference proceedings (pdf)
 SciPy2008 proceedings