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 Marius Kloft'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 Marius Kloft with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marius Kloft more than expected).
This network shows the impact of papers produced by Marius Kloft. 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 Marius Kloft. The network helps show where Marius Kloft may publish in the future.
Co-authorship network of co-authors of Marius Kloft
This figure shows the co-authorship network connecting the top 25 collaborators of Marius Kloft.
A scholar is included among the top collaborators of Marius Kloft 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 Marius Kloft. Marius Kloft is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lei, Yunwen, et al.. (2021). Fine-grained Generalization Analysis of Inductive Matrix Completion. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 34.4 indexed citations
9.
Ruff, Lukas, et al.. (2021). Explainable Deep One-Class Classification. arXiv (Cornell University).9 indexed citations
10.
Kloft, Marius, et al.. (2020). Two-sample Testing Using Deep Learning. International Conference on Artificial Intelligence and Statistics. 1387–1398.1 indexed citations
11.
Lei, Yunwen, et al.. (2020). Sharper Generalization Bounds for Pairwise Learning.. University of Birmingham Research Portal (University of Birmingham). 33. 21236–21246.9 indexed citations
Wang, Siqi, Yijie Zeng, Xinwang Liu, et al.. (2019). Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. Neural Information Processing Systems. 32. 5960–5973.39 indexed citations
14.
Lei, Yunwen, et al.. (2018). Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning. Journal of Machine Learning Research. 19(38). 1–47.10 indexed citations
15.
Ruff, Lukas, Robert A. Vandermeulen, Lucas Deecke, et al.. (2018). Deep One-Class Classification. International Conference on Machine Learning. 4393–4402.484 indexed citations breakdown →
16.
Lei, Yunwen, Ürün Doǧan, Ding‐Xuan Zhou, & Marius Kloft. (2017). Generalization Error Bounds for Extreme Multi-class Classification.. arXiv (Cornell University).1 indexed citations
17.
Görnitz, Nico, Anne K. Porbadnigk, Alexander Binder, et al.. (2014). Learning and Evaluation in Presence of Non-i.i.d. Label Noise. Journal of Machine Learning Research. 33. 293–302.1 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.