Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A new approach to cross-modal multimedia retrieval
2010948 citationsEmanuele Coviello, Gert Lanckriet et al.profile →
Multiple kernel learning, conic duality, and the SMO algorithm
2004943 citationsGert Lanckriet, Michael I. Jordan et al.profile →
A Direct Formulation for Sparse PCA Using Semidefinite Programming
2007423 citationsLaurent El Ghaoui, Michael I. Jordan et al.profile →
On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval
2013323 citationsEmanuele Coviello, Gert Lanckriet et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Gert Lanckriet
Since
Specialization
Citations
This map shows the geographic impact of Gert Lanckriet's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gert Lanckriet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gert Lanckriet more than expected).
This network shows the impact of papers produced by Gert Lanckriet. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gert Lanckriet. The network helps show where Gert Lanckriet may publish in the future.
Co-authorship network of co-authors of Gert Lanckriet
This figure shows the co-authorship network connecting the top 25 collaborators of Gert Lanckriet.
A scholar is included among the top collaborators of Gert Lanckriet based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Gert Lanckriet. Gert Lanckriet is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Lanckriet, Gert, et al.. (2014). Efficient Learning of Mahalanobis Metrics for Ranking. International Conference on Machine Learning. 1980–1988.28 indexed citations
2.
Coviello, Emanuele, et al.. (2013). That was fast! Speeding up NN search of high dimensional distributions.. International Conference on Machine Learning. 468–476.2 indexed citations
3.
Lanckriet, Gert, et al.. (2013). Robust Structural Metric Learning. International Conference on Machine Learning. 615–623.62 indexed citations
4.
Tyree, Stephen, et al.. (2012). Non-linear Metric Learning. neural information processing systems. 25. 2573–2581.99 indexed citations
5.
Coviello, Emanuele, Gert Lanckriet, & Antoni B. Chan. (2012). The variational hierarchical EM algorithm for clustering hidden Markov models. Neural Information Processing Systems. 25. 404–412.10 indexed citations
Fukumizu, Kenji, Gert Lanckriet, & Bharath K. Sriperumbudur. (2011). Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint. Neural Information Processing Systems. 24. 1773–1781.14 indexed citations
9.
McFee, Brian & Gert Lanckriet. (2010). Metric Learning to Rank. eScholarship (California Digital Library). 775–782.188 indexed citations
10.
Sriperumbudur, Bharath K., Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, & Gert Lanckriet. (2010). Hilbert Space Embeddings and Metrics on Probability Measures. Journal of Machine Learning Research. 11(50). 1517–1561.220 indexed citations
11.
Sriperumbudur, Bharath K., Kenji Fukumizu, & Gert Lanckriet. (2010). On the relation between universality, characteristic kernels and RKHS embedding of measures. International Conference on Artificial Intelligence and Statistics. 9. 773–780.21 indexed citations
12.
Sriperumbudur, Bharath K., et al.. (2009). The Sparse Eigenvalue Problem. arXiv (Cornell University).1 indexed citations
13.
Fukumizu, Kenji, Arthur Gretton, Gert Lanckriet, Bernhard Schölkopf, & Bharath K. Sriperumbudur. (2009). Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. Neural Information Processing Systems. 22. 1750–1758.89 indexed citations
14.
Sriperumbudur, Bharath K., Arthur Gretton, Kenji Fukumizu, Gert Lanckriet, & Bernhard Schölkopf. (2008). Injective hilbert space embeddings of probability measures. Max Planck Institute for Plasma Physics. 111–122.73 indexed citations
15.
Cortes, Corinna, Arthur Gretton, Gert Lanckriet, Mehryar Mohri, & Afshin Rostamizadeh. (2008). Kernel Learning: Automatic Selection of Optimal Kernels. MPG.PuRe (Max Planck Society). 94.1 indexed citations
16.
Agarwal, Sameer, Josh Wills, Lawrence Cayton, et al.. (2007). Generalized Non-metric Multidimensional Scaling.. International Conference on Artificial Intelligence and Statistics. 11–18.79 indexed citations
17.
Turnbull, Douglas, et al.. (2007). Exploring the Semantic Annotation and Retrieval of Sound.3 indexed citations
18.
Lanckriet, Gert, Tijl De Bie, Nello Cristianini, Michael I. Jordan, & William Stafford Noble. (2004). A Framework for Genomic Data Fusion and its Application to Membrane Protein Prediction. Bioinformatics. 20(16).4 indexed citations
Ghaoui, Laurent El, Michael I. Jordan, & Gert Lanckriet. (2002). Robust Novelty Detection with Single-Class MPM. Neural Information Processing Systems. 15. 929–936.58 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.