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2018


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Design and Analysis of the NIPS 2016 Review Process

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.

Journal of Machine Learning Research, 2018, *equal contribution (article) Accepted

arXiv [BibTex]

2018

arXiv [BibTex]

2016


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Consistent change-point detection with kernels

Garreau, D., Arlot, S.

December 2016, (Submitted on 14 Dec 2016 (v1), last revised 29 Jun 2017 (this version, v3)) (article)

Abstract
In this paper we study the kernel change-point algorithm (KCP) proposed by Arlot, Celisse and Harchaoui (2012), which aims at locating an unknown number of change-points in the distribution of a sequence of independent data taking values in an arbitrary set. The change-points are selected by model selection with a penalized kernel empirical criterion. We provide a non-asymptotic result showing that, with high probability, the KCP procedure retrieves the correct number of change-points, provided that the constant in the penalty is well-chosen; in addition, KCP estimates the change-points location at the optimal rate. As a consequence, when using a characteristic kernel, KCP detects all kinds of change in the distribution (not only changes in the mean or the variance), and it is able to do so for complex structured data (not necessarily in ℝd). Most of the analysis is conducted assuming that the kernel is bounded; part of the results can be extended when we only assume a finite second-order moment.

arXiv:1612.04740 [BibTex]

2016

arXiv:1612.04740 [BibTex]