Most cited papers in Machine Learning and Algorithms

Machine Learning and Algorithms

What are the most cited papers in Machine Learning and Algorithms?

The most cited paper in Machine Learning and Algorithms is "XGBoost" (Tianqi Chen, Carlos Guestrin, 2016), with 53,418 citations. Next come "Dropout: a simple way to prevent neural networks from overfitting" (34,210) and "Greedy function approximation: A gradient boosting machine." (30,593).

Published TypeAccessCitations

Showing 50 of 98 papers. Every paper with more than 1,636 citations is listed.

Most cited papers, sortable by citations and release date
#	Paper
1	XGBoost Tianqi Chen, Carlos Guestrin Conference paperOpen access	8 Aug 2016	53,418
2	Dropout: a simple way to prevent neural networks from overfitting Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al.	2014	34,210
3	Greedy function approximation: A gradient boosting machine. Jerome H. Friedman · The Annals of Statistics	Oct 2001	30,593
4	Random sample consensus Martin A. Fischler, Robert C. Bolles · Communications of the ACM Open access	Jun 1981	26,036
5	A Survey on Transfer Learning Sinno Jialin Pan, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering	16 Oct 2009	23,834
6	A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting Yoav Freund, Robert E. Schapire · Journal of Computer and System Sciences Open access	Aug 1997	20,713
7	Auto-Encoding Variational Bayes Diederik P. Kingma, Max Welling · UvA-DARE (University of Amsterdam) PreprintOpen access	20 Dec 2013	15,541
8	A tutorial on support vector regression Alex J. Smola, Bernhard Schölkopf · Statistics and Computing	19 Jul 2004	13,458
9	Text categorization with Support Vector Machines: Learning with many relevant features Thorsten Joachims · Lecture notes in computer science Conference paper	1998	8,050
10	Experiments with a new boosting algorithm Yoav Freund, Robert E. Schapire	3 Jul 1996	7,566
11	An overview of statistical learning theory Vladimir N. Vapnik · IEEE Transactions on Neural Networks	1999	6,335
12	The complexity of theorem-proving procedures Stephen A Cook Conference paperOpen access	1971	6,257
13	Estimating the Support of a High-Dimensional Distribution Bernhard Schölkopf, John C. Platt, John S. Shawe-Taylor et al. · Neural Computation	Jul 2001	6,166
14	Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan Prescott Adams · arXiv (Cornell University) PreprintOpen access	13 Jun 2012	5,667
15	Combining labeled and unlabeled data with co-training Avrim L. Blum, Tom M. Mitchell Conference paperOpen access	24 Jul 1998	5,638
16	Curriculum learning Yoshua Bengio, Jérôme Louradour, Ronan Collobert et al. Conference paper	14 Jun 2009	5,226
17	ADASYN: Adaptive synthetic sampling approach for imbalanced learning Haibo He, Yang Bai, Edwardo A. Garcia et al. Conference paper	Jun 2008	4,590
18	A Theory of the Learnable Leslie Gabriel Valiant · ACM eBooks Book chapter	1984	4,219
19	Optimizing search engines using clickthrough data Thorsten Joachims Conference paper	23 Jul 2002	3,934
20	Technical Note: Q-Learning Christopher J. C. H. Watkins, Peter Dayan · Machine Learning	May 1992	3,822
21	Fast Effective Rule Induction William W. Cohen · Elsevier eBooks Book chapter	1995	3,813
22	Learning with Local and Global Consistency Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal et al. · MPG.PuRe (Max Planck Society) Conference paperOpen access	9 Dec 2003	3,736
23	A theory of learning from different domains Shai Ben-David, John C. Blitzer, Koby Crammer et al. · Machine Learning Open access	22 Oct 2009	3,645
24	A few useful things to know about machine learning Pedro Domingos · Communications of the ACM	25 Sep 2012	3,368
25	The class imbalance problem: A systematic study1 Nathalie Japkowicz, Shaju Stephen · Intelligent Data Analysis	15 Nov 2002	3,367
26	The Strength of Weak Learnability Robert E. Schapire · Machine Learning Open access	Jun 1990	3,356
27	Selection of relevant features and examples in machine learning Avrim L. Blum, Pat Langley · Artificial Intelligence Open access	Dec 1997	3,312
28	A survey of robot learning from demonstration Brenna Argall, Sonia Chernova, Manuela Veloso et al. · Robotics and Autonomous Systems	3 Dec 2008	3,299
29	The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo Matthew D. Homan, Andrew Gelman · arXiv (Cornell University) PreprintOpen access	2014	3,272
30	Siamese Neural Networks for One-shot Image Recognition Gregory E. Koch, Richard S. Zemel, Ruslan Salakhutdinov	2015	3,246
31	Algorithms for Hyper-Parameter Optimization James Bergstra, R. Bardenet, Yoshua Bengio et al. Open access	2011	3,182
32	Trust Region Policy Optimization John Schulman, Sergey Levine, Philipp Moritz et al. · arXiv (Cornell University) PreprintOpen access	19 Feb 2015	3,119
33	Understanding Machine Learning: From Theory To Algorithms Shai Shalev‐Shwartz, Shai Ben-David	2015	3,076
34	Incorporating non-local information into information extraction systems by Gibbs sampling Jenny Rose Finkel, Trond Grenager, Christopher D. Manning Conference paperOpen access	2005	3,051
35	A Practical Bayesian Framework for Backpropagation Networks David Mackay · Neural Computation	May 1992	2,977
36	Human-level concept learning through probabilistic program induction Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum · Science	11 Dec 2015	2,969
37	A Short Introduction to Boosting Yoav Freund, Robert E. Schapire	1999	2,944
38	Bandit Based Monte-Carlo Planning Levente Kocsis, Csaba Szepesvári · Lecture notes in computer science Conference paper	2006	2,928
39	Instance-based learning algorithms David W. Aha, Dennis F. Kibler, Marc K. Albert · Machine Learning Open access	1991	2,920
40	Learning to rank using gradient descent Chris J. C. Burges, Tal Shaked, Erin Renshaw et al. Conference paper	2005	2,812
41	Generalizing from a Few Examples Yaqing Wang, Quanming Yao, James Tin-Yau Kwok et al. · ACM Computing Surveys	12 Jun 2020	2,782
42	Greedy Randomized Adaptive Search Procedures Thomas A. Feo, Maurício G. C. Resende · Journal of Global Optimization	Mar 1995	2,762
43	Text Classification from Labeled and Unlabeled Documents using EM Kamal Nigam, Andrew Kachites McCallum, Sebastian Thrun et al. · Machine Learning Open access	May 2000	2,754
44	Graph Implementations for Nonsmooth Convex Programs Michael C. Grant, Stephen Boyd · Lecture notes in control and information sciences Book chapter	Dec 2007	2,753
45	Solving Multiclass Learning Problems via Error-Correcting Output Codes Tom Dietterich, Ghulum Bakiri · Journal of Artificial Intelligence Research Open access	1995	2,735
46	Distance Metric Learning with Application to Clustering with Side-Information Eric P. Xing, Michael I. Jordan, Stuart Russell et al.	2002	2,702
47	Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent Noah Simon, J. Friedman, Trevor Hastie et al. · Journal of Statistical Software Open access	2011	2,690
48	Random Features for Large-Scale Kernel Machines Ali Rahimi, Benjamin Recht	3 Dec 2007	2,633
49	Optimal Brain Damage Yann LeCun, John S. Denker, Sara A. Solla	1989	2,601
50	Ising formulations of many NP problems Andrew Lucas · Frontiers in Physics Open access	2014	2,596

Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.