Learning to Abstain from Binary Prediction

By Akshay Balsubramani. 2016

8 citations recorded by the source corpus.

A binary classifier capable of abstaining from making a label prediction has two goals in tension: minimizing errors, and avoiding abstaining unnecessarily often. In this work, we exactly characterize the best achievable tradeoff between these two goals in a general semi-supervised setting, given an ensemble of predictors of varying competence as well as unlabeled data on which we wish to predict or abstain. We give an algorithm for learning a classifier in this setting which trades off its errors with abstentions in a minimax optimal manner, is as efficient as linear learning and prediction, and is demonstrably practical. Our analysis extends to a large class of loss functions and other scenarios, including ensembles comprised of specialists that can themselves abstain.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xin Li
2. Fuxin Li
3. Nicholas Carlini
4. Cristian Sminchisescu
5. David A. Wagner
6. João Carreira

Citations in the corpus

• Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics (2016, background): “[1, 30] also focuses on classification with an abstain option.”