Learning the Truth From Only One Side of the Story

Heinrich Jiang, Qijia Jiang, Aldo Pacchiano

Abstract:

Learning under one-sided feedback (i.e., where
we only observe the labels for examples we
predicted positively on) is a fundamental problem in machine learning – applications include
lending and recommendation systems. Despite this, there has been surprisingly little
progress made in ways to mitigate the effects
of the sampling bias that arises. We focus on
generalized linear models and show that without adjusting for this sampling bias, the model
may converge suboptimally or even fail to converge to the optimal solution. We propose an
adaptive approach that comes with theoretical guarantees and show that it outperforms
several existing methods empirically. Our
method leverages variance estimation techniques to efficiently learn under uncertainty,
offering a more principled alternative compared to existing approaches.