Fetching the paper…
Reading the bibliography…
Detecting concept drift is a well known problem that affects production systems.
Computing Extremely Accurate Quantiles Using t-Digests
Ted Dunning and Otmar Ertl. 2019 · 1902
Earlier work this paper cites.
On a test of whether one of two random variables is stochastically larger than the other
HB Mann and DR Whitney. 1947 · 1947
Earlier work this paper cites.
The Kolmogorov-Smirnov test for goodness of fit
Frank J Massey Jr. 1951 · 1951
Earlier work this paper cites.
A test of goodness of fit
Theodore W Anderson and Donald A Darling. 1954 · 1954
Earlier work this paper cites.
Tests concerning random points on a circle. In Nederl. Akad. Wetensch. Proc. Ser. A , Vol. 63. 38–47
Nicolaas H Kuiper. 1960 · 1960
Earlier work this paper cites.
A simple sequentially rejective multiple test procedure
Sture Holm. 1979 · 1979
Earlier work this paper cites.
A Space-Efficient Recursive Procedure for Estimating a Quantile of an Unknown Distribution
L. Tierney. 1983 · 1983
Earlier work this paper cites.
Divergence measures based on the Shannon entropy
J. Lin. 1991 · 1991
Earlier work this paper cites.
Incremental Quantile Estimation for Massive Tracking. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’00) . ACM, New York, NY, USA, 516–522
Fei Chen, Diane Lambert, and José C. Pinheiro. 2000 · 2000
Earlier work this paper cites.
Learning with drift detection. In Brazilian symposium on artificial intelligence . Springer, 286–295
Joao Gama, Pedro Medas, Gladys Castillo, and Pedro Rodrigues. 2004 · 2004
Earlier work this paper cites.
Learning from time-changing data with adaptive windowing. In Proceedings of the 2007 SIAM international conference on data mining . SIAM, 443–448
Albert Bifet and Ricard Gavalda. 2007 · 2007
Earlier work this paper cites.
Exponentially Weighted Simultaneous Estimation of Several Quantiles
Valeriy Naumov and Olli Martikainen. 2007 · 2007
Earlier work this paper cites.
Knowledge discovery from data streams
Joao Gama. 2010 · 2010
Cited alongside, same era.
Change with delayed labeling: When is it detectable?. In 2010 IEEE International Conference on Data Mining Workshops . IEEE, 843–850
Indre Žliobaite. 2010 · 2010
Cited alongside, same era.
Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Cited alongside, same era.
Detecting Novel Associations in Large Data Sets
David N. Reshef, Yakir A. Reshef, Hilary K. Finucane, Sharon R. Grossman, Gilean McVean, Peter J. Turnbaugh, Eric S. Lander, Michael Mitzenmacher, and Pardis C. Sabeti. 2011 · 2011
Cited alongside, same era.
On evaluating stream learning algorithms
João Gama, Raquel Sebastião, and Pedro Pereira Rodrigues. 2013 · 2013
Cited alongside, same era.
A survey on concept drift adaptation
Google vizier: A service for black-box optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1487–1495
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D Sculley. 2017 · 2017
Later among the works it cites.
Autolearn—Automated feature generation and selection. In 2017 IEEE International Conference on Data Mining (ICDM) . IEEE, 217–226
Ambika Kaul, Saket Maheshwary, and Vikram Pudi. 2017 · 2017
Later among the works it cites.
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
Lars Kotthoff, Chris Thornton, Holger H Hoos, Frank Hutter, and Kevin Leyton-Brown. 2017 · 2017
Later among the works it cites.
Learning Feature Engineering for Classification.. In IJCAI . 2529–2535
Fatemeh Nargesian, Horst Samulowitz, Udayan Khurana, Elias B Khalil, and Deepak S Turaga. 2017 · 2017
Later among the works it cites.
On the reliable detection of concept drift from streaming unlabeled data
Tegjyot Singh Sethi and Mehmed Kantardzic. 2017 · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014 · 2014
Cited alongside, same era.
Gradient boosted feature selection. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 522–531
Zhixiang Xu, Gao Huang, Kilian Q Weinberger, and Alice X Zheng. 2014 · 2014
Cited alongside, same era.
Efficient and robust automated machine learning. In Advances in neural information processing systems . 2962–2970
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015 · 2015
Cited alongside, same era.
Fast unsupervised online drift detection using incremental kolmogorov-smirnov test. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1545–1554
Denis Moreira dos Reis, Peter Flach, Stan Matwin, and Gustavo Batista. 2016 · 2016
Cited alongside, same era.
Quantiles over data streams: experimental comparisons, new analyses, and further improvements
Ge Luo, Lu Wang, Ke Yi, and Graham Cormode. 2016 · 2016
Cited alongside, same era.
Towards automatically-tuned neural networks. In Workshop on Automatic Machine Learning . 58–65
Hector Mendoza, Aaron Klein, Matthias Feurer, Jost Tobias Springenberg, and Frank Hutter. 2016 · 2016
Cited alongside, same era.
Adanet: Adaptive structural learning of artificial neural networks. In Proceedings of the 34th International Conference on Machine Learning, vol. 70 . JMLR, 874–883
Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, and Scott Yang. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
Towards AutoML in the presence of Drift: first results. In Workshop AutoML 2018@ ICML/IJCAI-ECAI
Jorge Madrid, Hugo Jair Escalante, Eduardo Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon, and Michèle Sebag. 2018 · 2018
Later among the works it cites.
Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 2279–2288
Charles Sutton, Timothy Hobson, James Geddes, and Rich Caruana. 2018 · 2018
Later among the works it cites.
Shujian Yu, Xiaoyang Wang, and Jose C Principe. 2018 · 2018
Later among the works it cites.
ML Health: Fitness Tracking for Production Models
Sindhu Ghanta, Sriram Subramanian, Lior Khermosh, Swaminathan Sundararaman, Harshil Shah, Yakov Goldberg, Drew Roselli, and Nisha Talagala. 2019 · 2019
Closest in time.
A new quantile tracking algorithm using a generalized exponentially weighted average of observations
Hugo Lewi Hammer, Anis Yazidi, and Håvard Rue. 2019 · 2019
Closest in time.
Evolutionary Neural AutoML for Deep Learning
Jason Liang, Elliot Meyerson, Babak Hodjat, Dan Fink, Karl Mutch, and Risto Miikkulainen. 2019 · 2019
Closest in time.