default search action
Geoffrey J. McLachlan
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [j73]Faïcel Chamroukhi
, Nhat Thien Pham
Functional mixtures-of-experts. Stat. Comput. 34(3): 98 (2024) - 2023
- [j72]You-Gan Wang
A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognit. 133: 108989 (2023) - 2022
- [j71]Daniel Ahfock
Statistical file-matching of non-Gaussian data: A game theoretic approach. Comput. Stat. Data Anal. 168: 107387 (2022) - [j70]Sharon X. Lee, Geoffrey J. McLachlan
An overview of skew distributions in model-based clustering. J. Multivar. Anal. 188: 104853 (2022) - [i10]Faïcel Chamroukhi, Nhat Thien Pham
Functional Mixtures-of-Experts. CoRR abs/2202.02249 (2022) - [i9]Ziyang Lyu
Some Simulation and Empirical Results for Semi-Supervised Learning of the Bayes Rule of Allocation. CoRR abs/2210.13785 (2022) - 2021
- [j69]Sharon X. Lee, Tsung-I Lin, Geoffrey J. McLachlan
Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. Adv. Data Anal. Classif. 15(2): 481-512 (2021) - [j68]Daniel Ahfock
Harmless label noise and informative soft-labels in supervised classification. Comput. Stat. Data Anal. 161: 107253 (2021) - [j67]Mohadeseh Alsadat Farzammehr, Mohammad Reza Zadkarami
Skew-normal generalized spatial panel data model. Commun. Stat. Simul. Comput. 50(11): 3286-3314 (2021) - [j66]Daniel Ahfock
Data fusion using factor analysis and low-rank matrix completion. Stat. Comput. 31(5): 58 (2021) - [j65]Sharon X. Lee, Geoffrey J. McLachlan
Multi-node Expectation-Maximization algorithm for finite mixture models. Stat. Anal. Data Min. 14(4): 297-304 (2021) - [i8]Daniel Ahfock, Geoffrey J. McLachlan:
Harmless label noise and informative soft-labels in supervised classification. CoRR abs/2104.02872 (2021) - [i7]Daniel Ahfock, Geoffrey J. McLachlan:
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review. CoRR abs/2104.04046 (2021) - 2020
- [j64]Hien Duy Nguyen
Mini-batch learning of exponential family finite mixture models. Stat. Comput. 30(4): 731-748 (2020) - [j63]Daniel Ahfock
An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified. Stat. Comput. 30(6): 1779-1790 (2020) - [i6]Geoffrey J. McLachlan, Daniel Ahfock:
Estimation of Classification Rules from Partially Classified Data. CoRR abs/2004.06237 (2020) - [i5]TrungTin Nguyen
An l1-oracle inequality for the Lasso in mixture-of-experts regression models. CoRR abs/2009.10622 (2020)
2010 – 2019
- 2019
- [j62]Sharon X. Lee
PPEM: Privacy-preserving EM learning for mixture models. Concurr. Comput. Pract. Exp. 31(24) (2019) - [j61]Shu-Kay Ng
Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework. Pattern Recognit. 88: 261-271 (2019) - [j60]Cinzia Viroli
Deep Gaussian mixture models. Stat. Comput. 29(1): 43-51 (2019) - 2018
- [j59]Luke R. Lloyd-Jones
A globally convergent algorithm for lasso-penalized mixture of linear regression models. Comput. Stat. Data Anal. 119: 19-38 (2018) - [j58]Hien Duy Nguyen
Randomized mixture models for probability density approximation and estimation. Inf. Sci. 467: 135-148 (2018) - [j57]Andrew T. Jones, Hien Duy Nguyen
logKDE: log-transformed kernel density estimation. J. Open Source Softw. 3(28): 870 (2018) - [j56]Hien Duy Nguyen
Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling. Stat. Anal. Data Min. 11(1): 5-16 (2018) - [j55]Sharon X. Lee
A Block EM Algorithm for Multivariate Skew Normal and Skew $t$ -Mixture Models. IEEE Trans. Neural Networks Learn. Syst. 29(11): 5581-5591 (2018) - [c32]Hien D. Nguyen
Positive Data Kernel Density Estimation via the LogKDE Package for R. AusDM 2018: 269-280 - 2017
- [j54]Hien Duy Nguyen
Maximum Pseudolikelihood Estimation for Model-Based Clustering of Time Series Data. Neural Comput. 29(4): 990-1020 (2017) - [c31]Kaleb L. Leemaqz, Sharon X. Lee, Geoffrey J. McLachlan
Private Distributed Three-Party Learning of Gaussian Mixture Models. ATIS 2017: 75-87 - [c30]Kaleb L. Leemaqz, Sharon X. Lee, Geoffrey J. McLachlan
Corruption-Resistant Privacy Preserving Distributed EM Algorithm for Model-Based Clustering. TrustCom/BigDataSE/ICESS 2017: 1082-1089 - [i4]Hien D. Nguyen, Geoffrey J. McLachlan:
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization-Minimization Algorithm Approach. CoRR abs/1705.04651 (2017) - [i3]Cinzia Viroli, Geoffrey J. McLachlan:
Deep Gaussian Mixture Models. CoRR abs/1711.06929 (2017) - 2016
- [j53]Hien Duy Nguyen
Mixtures of spatial spline regressions for clustering and classification. Comput. Stat. Data Anal. 93: 76-85 (2016) - [j52]Hien Duy Nguyen
Laplace mixture of linear experts. Comput. Stat. Data Anal. 93: 177-191 (2016) - [j51]Hien Duy Nguyen
Maximum likelihood estimation of triangular and polygonal distributions. Comput. Stat. Data Anal. 102: 23-36 (2016) - [j50]Hien Duy Nguyen
Linear mixed models with marginally symmetric nonparametric random effects. Comput. Stat. Data Anal. 103: 151-169 (2016) - [j49]Daniel Ahfock
Partial identification in the statistical matching problem. Comput. Stat. Data Anal. 104: 79-90 (2016) - [j48]Tsung-I Lin, Geoffrey J. McLachlan
Extending mixtures of factor models using the restricted multivariate skew-normal distribution. J. Multivar. Anal. 143: 398-413 (2016) - [j47]Hien Duy Nguyen
A Universal Approximation Theorem for Mixture-of-Experts Models. Neural Comput. 28(12): 2585-2593 (2016) - [j46]Sharon X. Lee, Geoffrey J. McLachlan
Finite mixtures of canonical fundamental skew t-distributions - The unification of the restricted and unrestricted skew t-mixture models. Stat. Comput. 26(3): 573-589 (2016) - [j45]Hien Duy Nguyen
A Block Minorization-Maximization Algorithm for Heteroscedastic Regression. IEEE Signal Process. Lett. 23(8): 1131-1135 (2016) - [c29]Sharon X. Lee, Geoffrey J. McLachlan
Unsupervised Component-Wise EM Learning for Finite Mixtures of Skew t-distributions. ADMA 2016: 692-699 - [c28]Shu-Kay Ng
Finding group structures in "Big Data" in healthcare research using mixture models. BIBM 2016: 1214-1219 - [c27]Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan
A Simple Parallel EM Algorithm for Statistical Learning via Mixture Models. DICTA 2016: 1-8 - [i2]Sharon X. Lee, Kaleb L. Leemaqz, Geoffrey J. McLachlan:
A block EM algorithm for multivariate skew normal and skew t-mixture models. CoRR abs/1608.02797 (2016) - 2015
- [j44]Hien Duy Nguyen
Maximum likelihood estimation of Gaussian mixture models without matrix operations. Adv. Data Anal. Classif. 9(4): 371-394 (2015) - 2014
- [j43]Dankmar Böhning, Christian Hennig, Geoffrey J. McLachlan, Paul D. McNicholas
The 2nd special issue on advances in mixture models. Comput. Stat. Data Anal. 71: 1-2 (2014) - [j42]Shu-Kay Ng
Mixture models for clustering multilevel growth trajectories. Comput. Stat. Data Anal. 71: 43-51 (2014) - [j41]Sharon X. Lee, Geoffrey J. McLachlan
Finite mixtures of multivariate skew t-distributions: some recent and new results. Stat. Comput. 24(2): 181-202 (2014) - [j40]Hien Duy Nguyen
False Discovery Rate Control in Magnetic Resonance Imaging Studies via Markov Random Fields. IEEE Trans. Medical Imaging 33(8): 1735-1748 (2014) - [j39]Geoffrey J. McLachlan
On the number of components in a Gaussian mixture model. WIREs Data Mining Knowl. Discov. 4(5): 341-355 (2014) - [c26]Hien Duy Nguyen
Asymptotic inference for hidden process regression models. SSP 2014: 256-259 - 2013
- [j38]Sharon X. Lee, Geoffrey J. McLachlan
On mixtures of skew normal and skew t-distributions. Adv. Data Anal. Classif. 7(3): 241-266 (2013) - [j37]Kaye E. Basford
On the classification of microarray gene-expression data. Briefings Bioinform. 14(4): 402-410 (2013) - [j36]Sharon X. Lee, Geoffrey J. McLachlan
Model-based clustering and classification with non-normal mixture distributions. Stat. Methods Appl. 22(4): 427-454 (2013) - [j35]Sharon X. Lee, Geoffrey J. McLachlan
Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". Stat. Methods Appl. 22(4): 473-479 (2013) - [c25]Mingzhu Sun, Geoffrey J. McLachlan
A common factor-analytic model for classification. BIBM 2013: 19-24 - [c24]Shu-Kay Ng
Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. BIBM 2013: 267-272 - [c23]Hien Duy Nguyen
Spatial False Discovery Rate Control for Magnetic Resonance Imaging Studies. DICTA 2013: 1-8 - [e1]Guo-Zheng Li, Sunghoon Kim, Michael Hughes, Geoffrey J. McLachlan, Hongye Sun, Xiaohua Hu, Habtom W. Ressom, Baoyan Liu, Michael N. Liebman:
2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, December 18-21, 2013. IEEE Computer Society 2013, ISBN 978-1-4799-1309-1 [contents] - 2012
- [j34]Kui Wang, Shu-Kay Ng
Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects. BMC Bioinform. 13: 300 (2012) - [j33]Gabor Melli, Xindong Wu, Paul Beinat, Francesco Bonchi, Longbing Cao
Top-10 Data Mining Case Studies. Int. J. Inf. Technol. Decis. Mak. 11(2): 389-400 (2012) - [p2]Geoffrey J. McLachlan
An Enduring Interest in Classification: Supervised and Unsupervised. Journeys to Data Mining 2012: 147-171 - 2011
- [j32]Jangsun Baek, Geoffrey J. McLachlan
Mixtures of common t-factor analyzers for clustering high-dimensional microarray data. Bioinform. 27(9): 1269-1276 (2011) - [j31]Vladimir Nikulin, Tian-Hsiang Huang, Geoffrey J. McLachlan
Classification of High-Dimensional microarray Data with a Two-Step Procedure via a Wilcoxon Criterion and Multilayer Perceptron. Int. J. Comput. Intell. Appl. 10(1): 1-14 (2011) - 2010
- [j30]Kim-Anh Lê Cao
Integrative mixture of experts to combine clinical factors and gene markers. Bioinform. 26(9): 1192-1198 (2010) - [j29]Jangsun Baek, Geoffrey J. McLachlan
Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data. IEEE Trans. Pattern Anal. Mach. Intell. 32(7): 1298-1309 (2010) - [c22]Vladimir Nikulin, Tian-Hsiang Huang, Geoffrey J. McLachlan
A comparative study of two matrix factorization methods applied to the classification of gene expression data. BIBM 2010: 618-621 - [c21]Vladimir Nikulin, Geoffrey J. McLachlan:
On the Gradient-based Algorithm for Matrix Factorization Applied to Dimensionality Reduction. BIOINFORMATICS 2010: 147-152 - [c20]Geoffrey J. McLachlan:
Assessing the Significance of Groups in High-Dimensional Data. ICDM 2010: 6 - [c19]Vladimir Nikulin, Geoffrey J. McLachlan
Identifying fiber bundles with regularised к-means clustering applied to the grid-based data. IJCNN 2010: 1-8 - [c18]Saumyadipta Pyne, Xinli Hu, Kui Wang, Elizabeth Rossin, Tsung-I Lin, Lisa Maier, Clare Baecher-Allan, Geoffrey J. McLachlan, Pablo Tamayo, David Hafler, Philip L. De Jager, Jill P. Mesirov:
Automated High-Dimensional Flow Cytometric Data Analysis. RECOMB 2010: 577 - [i1]Vladimir Nikulin, Tian-Hsiang Huang, Shu-Kay Ng, Suren I. Rathnayake, Geoffrey J. McLachlan:
A Very Fast Algorithm for Matrix Factorization. CoRR abs/1011.0506 (2010)
2000 – 2009
- 2009
- [c17]Vladimir Nikulin, Geoffrey J. McLachlan
Ensemble Approach for the Classification of Imbalanced Data. Australasian Conference on Artificial Intelligence 2009: 291-300 - [c16]Vladimir Nikulin, Geoffrey J. McLachlan
Penalized Principal Component Analysis of Microarray Data. CIBB 2009: 82-96 - [c15]Kui Wang, Shu-Kay Ng
Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data. DICTA 2009: 526-531 - [c14]Vladimir Nikulin, Geoffrey J. McLachlan:
Classification of Imbalanced Marketing Data with Balanced Random Sets. KDD Cup 2009: 89-100 - 2008
- [j28]Murray A. Jorgensen, Geoffrey J. McLachlan
Wallace's Approach to Unsupervised Learning: The Snob Program. Comput. J. 51(5): 571-578 (2008) - [j27]Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan
Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1): 1-37 (2008) - [c13]Geoffrey J. McLachlan
Clustering of High-Dimensional Data via Finite Mixture Models. GfKl 2008: 33-44 - 2007
- [j26]Shu-Kay Ng
Extension of mixture-of-experts networks for binary classification of hierarchical data. Artif. Intell. Medicine 41(1): 57-67 (2007) - [j25]Jangsun Baek, Young Sook Son, Geoffrey J. McLachlan
Segmentation and intensity estimation of microarray images using a gamma-t mixture model. Bioinform. 23(4): 458-465 (2007) - [j24]Kui Wang
Multilevel survival modelling of recurrent urinary tract infections. Comput. Methods Programs Biomed. 87(3): 225-229 (2007) - [j23]Geoffrey J. McLachlan, Richard W. Bean
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution. Comput. Stat. Data Anal. 51(11): 5327-5338 (2007) - [j22]Kui Wang
Two-component Poisson mixture regression modelling of count data with bivariate random effects. Math. Comput. Model. 46(11-12): 1468-1476 (2007) - [c12]Vladimir Nikulin, Geoffrey J. McLachlan
Merging Algorithm to Reduce Dimensionality in Application to Web-Mining. Australian Conference on Artificial Intelligence 2007: 755-761 - 2006
- [j21]Shu-Kay Ng
An incremental EM-based learning approach for on-line prediction of hospital resource utilization. Artif. Intell. Medicine 36(3): 257-267 (2006) - [j20]Geoffrey J. McLachlan
A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinform. 22(13): 1608-1615 (2006) - [j19]Shu-Kay Ng
A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinform. 22(14): 1745-1752 (2006) - [j18]Liat Ben-Tovim Jones, Richard Bean
Mixture Models for Detecting Differentially Expressed Genes in Microarrays. Int. J. Neural Syst. 16(5): 353-362 (2006) - 2005
- [c11]Shu-Kay Ng
Normalized Gaussian Networks with Mixed Feature Data. Australian Conference on Artificial Intelligence 2005: 879-882 - [c10]Richard Bean, Geoffrey J. McLachlan:
Cluster Analysis of High-Dimensional Data: A Case Study. IDEAL 2005: 302-310 - [c9]Liat Ben-Tovim Jones, Richard Bean, Geoffrey J. McLachlan, Justin Xi Zhu:
Application of Mixture Models to Detect Differentially Expressed Genes. IDEAL 2005: 422-431 - 2004
- [j17]Shu-Kay Ng
Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognit. 37(8): 1573-1589 (2004) - [j16]Shu-Kay Ng
Using the EM algorithm to train neural networks: misconceptions and a new algorithm for multiclass classification. IEEE Trans. Neural Networks 15(3): 738-749 (2004) - [c8]Geoffrey J. McLachlan, Soong Chang, Jess Mar, Christophe Ambroise, Justin Xi Zhu:
On the Simultaneous Use of Clinical and Microarray Expression Data in the Cluster Analysis of Tissue Samples. APBC 2004: 167-171 - 2003
- [j15]Geoffrey J. McLachlan
Modelling high-dimensional data by mixtures of factor analyzers. Comput. Stat. Data Anal. 41(3-4): 379-388 (2003) - [j14]J. C. Mar, Geoffrey J. McLachlan
Model-Based Clustering In Gene Expression Microarrays: An Application To Breast Cancer Data. Int. J. Softw. Eng. Knowl. Eng. 13(6): 579-592 (2003) - [j13]Shu-Kay Ng
On the choice of the number of blocks with the incremental EM algorithm for the fitting of normal mixtures. Stat. Comput. 13(1): 45-55 (2003) - [c7]J. C. Mar, Geoffrey J. McLachlan:
Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data. APBC 2003: 139-144 - [c6]Shu-Kay Ng, Geoffrey J. McLachlan:
Robust Estimation in Gaussian Mixtures Using Multiresolution Kd-trees. DICTA 2003: 145-154 - 2002
- [j12]Geoffrey J. McLachlan
A mixture model-based approach to the clustering of microarray expression data. Bioinform. 18(3): 413-422 (2002) - [j11]Igor V. Cadez, Padhraic Smyth
Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Mach. Learn. 47(1): 7-34 (2002) - 2000
- [b1]Geoffrey J. McLachlan
Finite Mixture Models. Wiley Series in Probability and Statistics, Wiley 2000, ISBN 978-0-47100626-8, pp. 1-427 - [j10]David Peel, Geoffrey J. McLachlan
Robust mixture modelling using the t distribution. Stat. Comput. 10(4): 339-348 (2000) - [c5]Geoffrey J. McLachlan, David Peel:
Mixtures of Factor Analyzers. ICML 2000: 599-606
1990 – 1999
- 1999
- [c4]Igor V. Cadez, Christine E. McLaren, Padhraic Smyth, Geoffrey J. McLachlan:
Hierarchical Models for Screening of Iron Deficiency Anemia. ICML 1999: 77-86 - 1998
- [c3]Geoffrey J. McLachlan, David Peel:
MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models. ICPR 1998: 553-557 - [c2]A. J. Feelders, Soong Chang, Geoffrey J. McLachlan:
Mining in the Presence of Selectivity Bias and its Application to Reject Inference. KDD 1998: 199-203 - [c1]Geoffrey J. McLachlan, David Peel:
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions. SSPR/SPR 1998: 658-666 - 1996
- [j9]Geoffrey J. McLachlan
Maximum likelihood clustering via normal mixture models. Signal Process. Image Commun. 8(2): 105-111 (1996)
1980 – 1989
- 1989
- [j8]Charles R. O. Lawoko, Geoffrey J. McLachlan
Bias associated with the discriminant analysis approach to the estimation of mixing proportions. Pattern Recognit. 22(6): 763-766 (1989) - 1988
- [j7]Charles R. O. Lawoko, Geoffrey J. McLachlan
Further results on discrimination with autocorrelated observations. Pattern Recognit. 21(1): 69-72 (1988) - 1986
- [j6]Charles R. O. Lawoko, Geoffrey J. McLachlan
Asymptotic error rates of the W and Z statistics when the training observations are dependent. Pattern Recognit. 19(6): 467-471 (1986) - 1985
- [j5]Charles R. O. Lawoko, Geoffrey J. McLachlan:
Discrimination with autocorrelated observations. Pattern Recognit. 18(2): 145-149 (1985) - 1983
- [j4]Charles R. O. Lawoko, Geoffrey J. McLachlan
Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function. Pattern Recognit. 16(1): 119-121 (1983) - 1982
- [p1]Geoffrey J. McLachlan:
9 The classification and mixture maximum likelihood approaches to cluster analysis. Classification, Pattern Recognition and Reduction of Dimensionality 1982: 199-208 - 1980
- [j3]S. Ganesalingam, Geoffrey J. McLachlan:
Error rate estimation on the basis of posterior probabilities. Pattern Recognit. 12(6): 405-413 (1980)
1970 – 1979
- 1977
- [j2]Geoffrey J. McLachlan
A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification. Pattern Recognit. 9(3): 147-149 (1977) - 1976
- [j1]Geoffrey J. McLachlan
Further results on the effect of intraclass correlation among training samples in discriminant analysis. Pattern Recognit. 8(4): 273-275 (1976)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:26 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: