Countries citing papers authored by Aurélie Lozano
Since
Specialization
Citations
This map shows the geographic impact of Aurélie Lozano'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 Aurélie Lozano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aurélie Lozano more than expected).
This network shows the impact of papers produced by Aurélie Lozano. 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 Aurélie Lozano. The network helps show where Aurélie Lozano may publish in the future.
Co-authorship network of co-authors of Aurélie Lozano
This figure shows the co-authorship network connecting the top 25 collaborators of Aurélie Lozano.
A scholar is included among the top collaborators of Aurélie Lozano 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 Aurélie Lozano. Aurélie Lozano is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lozano, Aurélie, et al.. (2021). Adaptive Proximal Gradient Methods for Structured Neural Networks. Neural Information Processing Systems. 34.2 indexed citations
3.
Zheng, Peng, et al.. (2019). Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning.. International Conference on Machine Learning. 7242–7251.1 indexed citations
4.
Yang, Eunho, Aurélie Lozano, & Pradeep Ravikumar. (2015). Closed-form estimators for high-dimensional generalized linear models. Neural Information Processing Systems. 28. 586–594.2 indexed citations
Yang, Eunho, Aurélie Lozano, & Pradeep Ravikumar. (2014). Elementary Estimators for High-Dimensional Linear Regression. International Conference on Machine Learning. 388–396.11 indexed citations
9.
Kambadur, Prabhanjan & Aurélie Lozano. (2013). A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions. International Conference on Artificial Intelligence and Statistics. 351–359.1 indexed citations
Świrszcz, Grzegorz & Aurélie Lozano. (2012). Multi-level Lasso for Sparse Multi-task Regression. International Conference on Machine Learning. 595–602.46 indexed citations
Lozano, Aurélie, Grzegorz Świrszcz, & Naoki Abe. (2011). Group Orthogonal Matching Pursuit for Logistic Regression. International Conference on Artificial Intelligence and Statistics. 452–460.26 indexed citations
14.
Sindhwani, Vikas & Aurélie Lozano. (2011). Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels. Neural Information Processing Systems. 24. 2519–2527.10 indexed citations
15.
Sindhwani, Vikas & Aurélie Lozano. (2010). Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference. Neural Information Processing Systems. 23. 1486–1494.9 indexed citations
16.
Liu, Yan, Alexandru Niculescu-Mizil, Aurélie Lozano, & Yong Lu. (2010). Learning Temporal Causal Graphs for Relational Time-Series Analysis. International Conference on Machine Learning. 687–694.22 indexed citations
17.
Świrszcz, Grzegorz, Naoki Abe, & Aurélie Lozano. (2009). Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction. Neural Information Processing Systems. 22. 1150–1158.62 indexed citations
18.
Lozano, Aurélie, et al.. (2008). Cost-sensitive Boosting with p-norm Loss Functionsand its Applications. Kyushu University Institutional Repository (QIR) (Kyushu University). 12. 65–74.2 indexed citations
19.
Lozano, Aurélie, Sanjeev R. Kulkarni, & Robert E. Schapire. (2005). Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. Neural Information Processing Systems. 18. 819–826.22 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.