Maayan Harel

ICML Conference 2014 Conference Paper

Concept Drift Detection Through Resampling

• Maayan Harel
• Shie Mannor
• Ran El-Yaniv
• Koby Crammer

Detecting changes in data-streams is an important part of enhancing learning quality in dynamic environments. We devise a procedure for detecting concept drifts in data-streams that relies on analyzing the empirical loss of learning algorithms. Our method is based on obtaining statistics from the loss distribution by reusing the data multiple times via resampling. We present theoretical guarantees for the proposed procedure based on the stability of the underlying learning algorithms. Experimental results show that the detection method has high recall and precision, and performs well in the presence of noise.

NeurIPS Conference 2012 Conference Paper

The Perturbed Variation

• Maayan Harel
• Shie Mannor

We introduce a new discrepancy score between two distributions that gives an indication on their \emph{similarity}. While much research has been done to determine if two samples come from exactly the same distribution, much less research considered the problem of determining if two finite samples come from similar distributions. The new score gives an intuitive interpretation of similarity; it optimally perturbs the distributions so that they best fit each other. The score is defined between distributions, and can be efficiently estimated from samples. We provide convergence bounds of the estimated score, and develop hypothesis testing procedures that test if two data sets come from similar distributions. The statistical power of this procedures is presented in simulations. We also compare the score's capacity to detect similarity with that of other known measures on real data.

ICML Conference 2011 Conference Paper

Learning from Multiple Outlooks

• Maayan Harel
• Shie Mannor