Mixture Proportion Estimation and PU Learning:A Modern Approach

1 code implementation • NeurIPS 2021 • Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton

Formally, this task is broken down into two subtasks: (i) Mixture Proportion Estimation (MPE)---determining the fraction of positive examples in the unlabeled data; and (ii) PU-learning---given such an estimate, learning the desired positive-versus-negative classifier.

Temporal-Clustering Invariance in Irregular Healthcare Time Series

no code implementations • 27 Apr 2019 • Mohammad Taha Bahadori, Zachary Chase Lipton

We postulate that fine temporal detail, e. g., whether a series of blood tests are completed at once or in rapid succession should not alter predictions based on this data.

Data Augmentation Mortality Prediction +1

Thresholding Classifiers to Maximize F1 Score

1 code implementation • 8 Feb 2014 • Zachary Chase Lipton, Charles Elkan, Balakrishnan Narayanaswamy

As another special case, if the classifier is completely uninformative, then the optimal behavior is to classify all examples as positive.

Classification Decision Making +1