83 citations · 107 across the 6 of their papers we have counts for
6 papers
Leveraging Structure for Improved Classification of Grouped Biased Data
Daniel Zeiberg, Shantanu Jain, Predrag Radivojac
We consider semi-supervised binary classification for applications in which data points are naturally grouped (e.g., survey responses grouped by state) and the labeled data is bias…
New mixture models for decoy-free false discovery rate estimation in mass-spectrometry proteomics
Yisu Peng, Shantanu Jain, Yong Fuga Li +4
Motivation: Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, targ…
Identifiability of two-component skew normal mixtures with one known component
Shantanu Jain, Michael Levine, Predrag Radivojac +1
We give sufficient identifiability conditions for estimating mixing proportions in two-component mixtures of skew normal distributions with one known component. We consider the uni…
Recovering True Classifier Performance in Positive-Unlabeled Learning
Shantanu Jain, Martha White, Predrag Radivojac
A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier…
Estimating the class prior and posterior from noisy positives and unlabeled data
Shantanu Jain, Martha White, Predrag Radivojac
We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dim…
Nonparametric semi-supervised learning of class proportions
Shantanu Jain, Martha White, Michael W. Trosset +1
The problem of developing binary classifiers from positive and unlabeled data is often encountered in machine learning. A common requirement in this setting is to approximate poste…