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.
Countries citing papers authored by Simon Lacoste-Julien
Since
Specialization
Citations
This map shows the geographic impact of Simon Lacoste-Julien'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 Simon Lacoste-Julien with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simon Lacoste-Julien more than expected).
Fields of papers citing papers by Simon Lacoste-Julien
This network shows the impact of papers produced by Simon Lacoste-Julien. 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 Simon Lacoste-Julien. The network helps show where Simon Lacoste-Julien may publish in the future.
Co-authorship network of co-authors of Simon Lacoste-Julien
This figure shows the co-authorship network connecting the top 25 collaborators of Simon Lacoste-Julien.
A scholar is included among the top collaborators of Simon Lacoste-Julien 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 Simon Lacoste-Julien. Simon Lacoste-Julien is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gidel, Gauthier, et al.. (2020). A Closer Look at the Optimization Landscapes of Generative Adversarial Networks. International Conference on Learning Representations.4 indexed citations
4.
Scieur, Damien, et al.. (2020). Accelerating Smooth Games by Manipulating Spectral Shapes.. International Conference on Artificial Intelligence and Statistics. 1705–1715.
5.
Mitliagkas, Ioannis, et al.. (2020). A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.. International Conference on Artificial Intelligence and Statistics. 2863–2873.7 indexed citations
6.
Mitliagkas, Ioannis, et al.. (2019). A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games.. arXiv (Cornell University).2 indexed citations
7.
Chavdarova, Tatjana, Gauthier Gidel, François Fleuret, & Simon Lacoste-Julien. (2019). Reducing Noise in GAN Training with Variance Reduced Extragradient. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 32. 391–401.5 indexed citations
8.
Larochelle, Hugo, et al.. (2019). Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification.. arXiv (Cornell University).6 indexed citations
9.
Gidel, Gauthier, Fabián Pedregosa, & Simon Lacoste-Julien. (2018). Frank-Wolfe Splitting via Augmented Lagrangian Method. International Conference on Artificial Intelligence and Statistics. 1456–1465.1 indexed citations
10.
Gidel, Gauthier, et al.. (2018). A Variational Inequality Perspective on Generative Adversarial Networks. arXiv (Cornell University).8 indexed citations
11.
Oyallon, Edouard, Sergey Zagoruyko, Nikos Komodakis, et al.. (2018). Scattering Networks for Hybrid Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(9). 2208–2221.40 indexed citations
12.
Gidel, Gauthier, Tony Jebara, & Simon Lacoste-Julien. (2017). Frank-Wolfe Algorithms for Saddle Point Problems. HAL (Le Centre pour la Communication Scientifique Directe).4 indexed citations
Krishnan, Rahul G., Simon Lacoste-Julien, & David Sontag. (2015). Barrier Frank-Wolfe for marginal inference. HAL (Le Centre pour la Communication Scientifique Directe). 28. 532–540.1 indexed citations
15.
Lacoste-Julien, Simon, Martin Jaggi, Mark Schmidt, & Patrick Pletscher. (2013). Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. Infoscience (Ecole Polytechnique Fédérale de Lausanne).24 indexed citations
16.
Lacoste-Julien, Simon, Martin Jaggi, Mark Schmidt, & Patrick Pletscher. (2012). Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. arXiv (Cornell University).9 indexed citations
17.
Lacoste-Julien, Simon, Ferenc Huszár, & Zoubin Ghahramani. (2011). Approximate inference for the loss-calibrated Bayesian. Cambridge University Engineering Department Publications Database. 416–424.11 indexed citations
18.
Jordan, Michael I. & Simon Lacoste-Julien. (2009). Discriminative machine learning with structure. 114–114.6 indexed citations
Taskar, Ben, Simon Lacoste-Julien, & Michael I. Jordan. (2005). Structured Prediction via the Extragradient Method. Neural Information Processing Systems. 18. 1345–1352.39 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.