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Leveraging Uncertainty Estimates To Improve Classifier Performance
Binary classification involves predicting the label of an instance based on whether the model score for the positive class exceeds a threshold chosen based on the application requirements (e. g., maximizing recall for a precision bound).
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels
The latent feature relational model (LFRM) is a generative model for graph-structured data to learn a binary vector representation for each node in the graph.
Generalized Zero-Shot Learning via Synthesized Examples
Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero-shot learning settings.
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