Gregory Plumb

981 total citations · 2 hit papers
6 papers, 515 citations indexed

About

Gregory Plumb is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mathematics. According to data from OpenAlex, Gregory Plumb has authored 6 papers receiving a total of 515 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Computational Mathematics. Recurrent topics in Gregory Plumb's work include Machine Learning and Data Classification (4 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Adversarial Robustness in Machine Learning (2 papers). Gregory Plumb is often cited by papers focused on Machine Learning and Data Classification (4 papers), Explainable Artificial Intelligence (XAI) (4 papers) and Adversarial Robustness in Machine Learning (2 papers). Gregory Plumb collaborates with scholars based in United States. Gregory Plumb's co-authors include Ameet Talwalkar, Joon Sik Kim, Valerie Chen, Jeffrey Li, Risi Kondor, Ángel Alexander Cabrera and Vikas Singh and has published in prestigious journals such as Communications of the ACM, Journal of Machine Learning Research and Queue.

In The Last Decade

Gregory Plumb

6 papers receiving 484 citations

Hit Papers

align trajectories

What are hit papers?

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.

Interpretable Machine Learning

2021 367 citations Valerie Chen, Jeffrey Li et al. Queue profile →
Interpretable machine learning

2022 138 citations Valerie Chen, Jeffrey Li et al. Communications of the ACM profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Gregory Plumb United States	4	237	37	34	31	27	6	515
Jeffrey Li United States	4	234 1.0×	37 1.0×	34 1.0×	30 1.0×	26 1.0×	5	524
Janek Thomas Germany	7	205 0.9×	33 0.9×	39 1.1×	38 1.2×	27 1.0×	15	606
Stefan Coors Germany	6	200 0.8×	29 0.8×	58 1.7×	24 0.8×	29 1.1×	7	739
Valerie Chen United States	8	295 1.2×	43 1.2×	57 1.7×	35 1.1×	26 1.0×	22	719
Martin Binder Germany	5	190 0.8×	27 0.7×	60 1.8×	24 0.8×	28 1.0×	10	693
Eduardo Soares Brazil	8	354 1.5×	57 1.5×	45 1.3×	73 2.4×	31 1.1×	30	734
Haiyang Huang China	5	261 1.1×	20 0.5×	20 0.6×	48 1.5×	14 0.5×	13	538
Lesia Semenova United States	4	276 1.2×	21 0.6×	18 0.5×	33 1.1×	11 0.4×	5	490
Shaoze Cui China	11	154 0.6×	32 0.9×	30 0.9×	15 0.5×	12 0.4×	15	469
Eduardo C. Garrido‐Merchán Spain	10	156 0.7×	54 1.5×	23 0.7×	12 0.4×	18 0.7×	21	460

Countries citing papers authored by Gregory Plumb

Since Specialization

Citations

This map shows the geographic impact of Gregory Plumb'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 Gregory Plumb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gregory Plumb more than expected).

Fields of papers citing papers by Gregory Plumb

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gregory Plumb. 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 Gregory Plumb. The network helps show where Gregory Plumb may publish in the future.

Co-authorship network of co-authors of Gregory Plumb

This figure shows the co-authorship network connecting the top 25 collaborators of Gregory Plumb. A scholar is included among the top collaborators of Gregory Plumb 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 Gregory Plumb. Gregory Plumb is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

6 of 6 papers shown

1.

Cabrera, Ángel Alexander, et al.. (2023). Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 11(1). 65–76. 5 indexed citations

2.

Chen, Valerie, et al.. (2022). Use-Case-Grounded Simulations for Explanation Evaluation. 1764–1775. 1 indexed citations

3.

Chen, Valerie, Jeffrey Li, Joon Sik Kim, Gregory Plumb, & Ameet Talwalkar. (2022). Interpretable machine learning. Communications of the ACM. 65(8). 43–50. 138 indexed citations breakdown →

4.

Chen, Valerie, Jeffrey Li, Joon Sik Kim, Gregory Plumb, & Ameet Talwalkar. (2021). Interpretable Machine Learning. Queue. 19(6). 28–56. 367 indexed citations breakdown →

5.

Plumb, Gregory, et al.. (2018). Supervised Local Modeling for Interpretability.. arXiv (Cornell University). 1 indexed citations

6.

Plumb, Gregory, et al.. (2015). S n FFT: a Julia toolkit for Fourier analysis of functions over permutations. Journal of Machine Learning Research. 16(1). 3469–3473. 3 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.

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