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Online Learning for Distribution-Free Prediction

Abstract · Mar 15, 2017 10:20 ·

cs-lg stat-co stat-ml

Arxiv Abstract

  • Dave Zachariah
  • Petre Stoica
  • Thomas B. Schön

We develop an online learning method for prediction, which is important in problems with large and/or streaming data sets. We formulate the learning approach using a covariance-fitting methodology, and show that the resulting predictor has desirable computational and distribution-free properties: It is implemented online with a runtime that scales linearly in the number of samples; has a constant memory requirement; avoids local minima problems; and prunes away redundant feature dimensions without relying on restrictive assumptions on the data distribution. In conjunction with the split conformal approach, it also produces distribution-free prediction confidence intervals in a computationally efficient manner. The method is demonstrated on both real and synthetic datasets.

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