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.
Robust fine-tuning of zero-shot models
2022208 citationsRebecca Roelofs, Ludwig Schmidt et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Ludwig Schmidt
Since
Specialization
Citations
This map shows the geographic impact of Ludwig Schmidt'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 Ludwig Schmidt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ludwig Schmidt more than expected).
This network shows the impact of papers produced by Ludwig Schmidt. 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 Ludwig Schmidt. The network helps show where Ludwig Schmidt may publish in the future.
Co-authorship network of co-authors of Ludwig Schmidt
This figure shows the co-authorship network connecting the top 25 collaborators of Ludwig Schmidt.
A scholar is included among the top collaborators of Ludwig Schmidt 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 Ludwig Schmidt. Ludwig Schmidt is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Taori, Rohan, et al.. (2021). Are We Learning Yet? A Meta Review of Evaluation Failures Across Machine Learning. Neural Information Processing Systems.17 indexed citations
4.
Taori, Rohan, Achal Dave, Vaishaal Shankar, et al.. (2020). Measuring Robustness to Natural Distribution Shifts in Image Classification. Neural Information Processing Systems. 33. 18583–18599.11 indexed citations
5.
Recht, Benjamin, Rebecca Roelofs, Ludwig Schmidt, & Vaishaal Shankar. (2019). Do ImageNet Classifiers Generalize to ImageNet. International Conference on Machine Learning. 5389–5400.38 indexed citations
6.
Roelofs, Rebecca, Vaishaal Shankar, Benjamin Recht, et al.. (2019). A Meta-Analysis of Overfitting in Machine Learning. Neural Information Processing Systems. 32. 9175–9185.53 indexed citations
7.
Taori, Rohan, Achal Dave, Vaishaal Shankar, et al.. (2019). When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet.2 indexed citations
8.
Schmidt, Ludwig, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, & Aleksander Mądry. (2018). Adversarially Robust Generalization Requires More Data. DSpace@MIT (Massachusetts Institute of Technology). 31. 5014–5026.56 indexed citations
9.
Diakonikolas, Ilias, et al.. (2017). Communication-Efficient Distributed Learning of Discrete Distributions. Neural Information Processing Systems. 30. 6391–6401.7 indexed citations
10.
Li, Jerry & Ludwig Schmidt. (2017). Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities. Conference on Learning Theory. 1302–1382.4 indexed citations
Li, Jerry, Aleksander Mądry, John Peebles, & Ludwig Schmidt. (2017). Towards Understanding the Dynamics of Generative Adversarial Networks.. arXiv (Cornell University).8 indexed citations
13.
Engstrom, Logan, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, & Aleksander Mądry. (2017). A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations. arXiv (Cornell University).99 indexed citations
14.
Acharya, Jayadev, Ilias Diakonikolas, Jerry Li, & Ludwig Schmidt. (2016). Fast algorithms for segmented regression. Edinburgh Research Explorer (University of Edinburgh). 2878–2886.4 indexed citations
15.
Hegde, Chinmay, Piotr Indyk, & Ludwig Schmidt. (2016). Fast recovery from a union of subspaces. DSpace@MIT (Massachusetts Institute of Technology). 29. 4394–4402.6 indexed citations
Hegde, Chinmay, Piotr Indyk, & Ludwig Schmidt. (2015). Fast Algorithms for Structured Sparsity. Bulletin of the European Association for Theoretical Computer Science. 3(117).10 indexed citations
18.
Hegde, Chinmay, Piotr Indyk, & Ludwig Schmidt. (2015). A Nearly-Linear Time Framework for Graph-Structured Sparsity. International Conference on Machine Learning. 4165–4169.33 indexed citations
Schlick, Christopher, et al.. (2001). Eine empirische Untersuchung zur Modellierung von Handlungsvorhersagen mit Hilfe dynamischer Bayes-Netze. RWTH Publications (RWTH Aachen).2 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.