An Adaptive Algorithm for Learning with Unknown Distribution Drift

Alessio Mazzetto, Eli Upfal

Keywords: statistical learning, learning theory, machine learning, supervised learning, non-stationary, transfer learning, distribution drift

TL;DR: We develop and analyze an adaptive method for learning given a sequence of independent observations from a drifting distribution.

Abstract: We develop and analyze a general technique for learning with an unknown distribution drift. Given a sequence of independent observations from the last $T$ steps of a drifting distribution, our algorithm agnostically learns a family of functions with respect to the current distribution at time $T$. Unlike previous work, our technique does not require prior knowledge about the magnitude of the drift. Instead, the algorithm adapts to the sample data. Without explicitly estimating the drift, the algorithm learns a family of functions with almost the same error as a learning algorithm that knows the magnitude of the drift in advance. Furthermore, since our algorithm adapts to the data, it can guarantee a better learning error than an algorithm that relies on loose bounds on the drift. We demonstrate the application of our technique in two fundamental learning scenarios: binary classification and linear regression.

Submission Number: 11697