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.
This map shows the geographic impact of Martin Jaggi'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 Martin Jaggi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Jaggi more than expected).
This network shows the impact of papers produced by Martin Jaggi. 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 Martin Jaggi. The network helps show where Martin Jaggi may publish in the future.
Co-authorship network of co-authors of Martin Jaggi
This figure shows the co-authorship network connecting the top 25 collaborators of Martin Jaggi.
A scholar is included among the top collaborators of Martin Jaggi 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 Martin Jaggi. Martin Jaggi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Karimireddy, Sai Praneeth, Martin Jaggi, Satyen Kale, et al.. (2021). Breaking the centralized barrier for cross-device federated learning. Neural Information Processing Systems. 34.25 indexed citations
4.
Shokri‐Ghadikolaei, Hossein, Sebastian U. Stich, & Martin Jaggi. (2021). LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 130. 3943–3951.1 indexed citations
5.
Yu, Kaicheng, et al.. (2020). Evaluating The Search Phase of Neural Architecture Search. Infoscience (Ecole Polytechnique Fédérale de Lausanne).55 indexed citations
6.
Pedregosa, Fabián, et al.. (2020). Linearly Convergent Frank-Wolfe without Line-Search.. International Conference on Artificial Intelligence and Statistics. 1–10.
7.
Lin, Tao, Lingjing Kong, Sebastian U. Stich, & Martin Jaggi. (2020). Ensemble Distillation for Robust Model Fusion in Federated Learning. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 33. 2351–2363.3 indexed citations
8.
Vogels, Thijs, Sai Praneeth Karimireddy, & Martin Jaggi. (2020). Practical Low-Rank Communication Compression in Decentralized Deep Learning. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 33. 14171–14181.14 indexed citations
9.
Mai, Florian, et al.. (2019). On the Tunability of Optimizers in Deep Learning. Infoscience (Ecole Polytechnique Fédérale de Lausanne).4 indexed citations
10.
Vogels, Thijs, Sai Praneeth Karimireddy, & Martin Jaggi. (2019). PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 32. 14236–14245.15 indexed citations
11.
Lin, T., et al.. (2018). End-to-End DNN Training with Block Floating Point Arithmetic. arXiv (Cornell University).6 indexed citations
12.
Pedregosa, Fabián, et al.. (2018). Step-Size Adaptivity in Projection-Free Optimization. arXiv (Cornell University).3 indexed citations
13.
Karimireddy, Sai Praneeth, Sebastian U. Stich, & Martin Jaggi. (2018). Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1204–1213.1 indexed citations
14.
Jaggi, Martin, et al.. (2018). COLA: Communication-Efficient Decentralized Linear Learning. arXiv (Cornell University). 4537–4547.1 indexed citations
15.
Musat, Claudiu, et al.. (2018). EmbedRank: Unsupervised Keyphrase Extraction using Sentence Embeddings.. arXiv (Cornell University).13 indexed citations
16.
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
Giesen, Joachim, et al.. (2012). Regularization Paths with Guarantees for Convex Semidefinite Optimization.. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 432–439.9 indexed citations
19.
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
20.
Jaggi, Martin, et al.. (2010). A Simple Algorithm for Nuclear Norm Regularized Problems. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 471–478.112 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.