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 Jeff Bilmes'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 Jeff Bilmes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Bilmes more than expected).
This network shows the impact of papers produced by Jeff Bilmes. 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 Jeff Bilmes. The network helps show where Jeff Bilmes may publish in the future.
Co-authorship network of co-authors of Jeff Bilmes
This figure shows the co-authorship network connecting the top 25 collaborators of Jeff Bilmes.
A scholar is included among the top collaborators of Jeff Bilmes 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 Jeff Bilmes. Jeff Bilmes is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wang, Shengjie, et al.. (2019). Fixing Mini-batch Sequences with Hierarchical Robust Partitioning. International Conference on Artificial Intelligence and Statistics. 3352–3361.3 indexed citations
Cotter, Andrew, et al.. (2018). Constrained Interacting Submodular Groupings. International Conference on Machine Learning. 1068–1077.2 indexed citations
6.
Zhou, Tianyi & Jeff Bilmes. (2018). Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity. International Conference on Learning Representations.12 indexed citations
7.
Wang, Shengjie, et al.. (2017). Training Compressed Fully-Connected Networks with a Density-Diversity Penalty. International Conference on Learning Representations.6 indexed citations
8.
Iyer, Rishabh, et al.. (2016). Algorithms for optimizing the ratio of submodular functions. International Conference on Machine Learning. 2751–2759.9 indexed citations
9.
Wang, Shengjie, Abdelrahman Mohamed, Rich Caruana, et al.. (2016). Analysis of Deep Neural Networks with Extended Data Jacobian Matrix. International Conference on Machine Learning. 718–726.8 indexed citations
10.
Iyer, Rishabh & Jeff Bilmes. (2015). Submodular Point Processes with Applications to Machine Learning. International Conference on Artificial Intelligence and Statistics. 388–397.6 indexed citations
11.
Wei, Kai, Rishabh Iyer, & Jeff Bilmes. (2015). Submodularity in Data Subset Selection and Active Learning. International Conference on Machine Learning. 1954–1963.104 indexed citations
Guillory, Andrew & Jeff Bilmes. (2011). Online Submodular Set Cover, Ranking, and Repeated Active Learning. Neural Information Processing Systems. 24. 1107–1115.9 indexed citations
14.
Jegelka, Stefanie & Jeff Bilmes. (2011). Online Submodular Minimization for Combinatorial Structures. Max Planck Institute for Plasma Physics. 345–352.8 indexed citations
15.
Lin, Hui & Jeff Bilmes. (2010). Multi-document Summarization via Budgeted Maximization of Submodular Functions. North American Chapter of the Association for Computational Linguistics. 912–920.212 indexed citations
16.
Subramanya, Amarnag & Jeff Bilmes. (2009). Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification. Neural Information Processing Systems. 22. 1803–1811.43 indexed citations
17.
Pentney, William, Matthai Philipose, & Jeff Bilmes. (2008). Structure learning on large scale common sense statistical models of human state. National Conference on Artificial Intelligence. 1389–1395.8 indexed citations
18.
Moore, Robert C., Jeff Bilmes, Jennifer Chu‐Carroll, & Mark Sanderson. (2006). Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers. North American Chapter of the Association for Computational Linguistics.27 indexed citations
Narasimhan, Mukund & Jeff Bilmes. (2004). Optimal sub-graphical models. Neural Information Processing Systems. 17. 961–968.2 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.