Fast Algorithms for Segmented Regression

Jayadev Acharya, Ilias Diakonikolas, J. Li, L. Schmidt

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We study the fixed design segmented regression problem: Given noisy samples from a piecewise linear function ff, we want to recover ff up to a desired accuracy in mean-squared error. Previous rigorous approaches for this problem rely on dynamic programming (DP) and, while sample efficient, have running time quadratic in the sample size. As our main contribution, we provide new sample near-linear time algorithms for the problem that -- while not being minimax optimal -- achieve a significantly better sample-time tradeoff on large datasets compared to the DP approach. Our experimental evaluation shows that, compared with the DP approach, our algorithms provide a convergence rate that is only off by a factor of 22 to 33, while achieving speedups of two orders of magnitude.
Original languageEnglish
Title of host publicationProceedings of the 33rd International Conference on Machine Learning (ICML 2016)
Place of PublicationNew York, USA
Number of pages9
Publication statusPublished - 24 Jun 2016
Event33rd International Conference on Machine Learning: ICML 2016 - New York, United States
Duration: 19 Jun 201624 Jun 2016

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


Conference33rd International Conference on Machine Learning
Abbreviated titleICML 2016
CountryUnited States
CityNew York
Internet address


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