Stephen Mussmann

550 total citations
10 papers, 93 citations indexed

About

Stephen Mussmann is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Condensed Matter Physics. According to data from OpenAlex, Stephen Mussmann has authored 10 papers receiving a total of 93 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 2 papers in Statistical and Nonlinear Physics and 1 paper in Condensed Matter Physics. Recurrent topics in Stephen Mussmann's work include Machine Learning and Algorithms (4 papers), Topic Modeling (4 papers) and Machine Learning and Data Classification (4 papers). Stephen Mussmann is often cited by papers focused on Machine Learning and Algorithms (4 papers), Topic Modeling (4 papers) and Machine Learning and Data Classification (4 papers). Stephen Mussmann collaborates with scholars based in United States. Stephen Mussmann's co-authors include Percy Liang, Arun Tejasvi Chaganty, Stefano Ermon, Robin Jia, Jure Leskovec, Baharan Mirzasoleiman, Peter Bailis, Christopher Yeh, Matei Zaharia and Jennifer Neville and has published in prestigious journals such as Proceedings of the VLDB Endowment, arXiv (Cornell University) and International Conference on Machine Learning.

In The Last Decade

Stephen Mussmann

9 papers receiving 85 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Stephen Mussmann United States	5	84	24	10	5	5	10	93
Prajjwal Bhargava United States	2	56 0.7×	14 0.6×	10 1.0×	6 1.2×	4 0.8×	3	68
Nora Kassner Germany	3	116 1.4×	39 1.6×	7 0.7×	4 0.8×	5 1.0×	10	132
J. Edward Hu United States	5	162 1.9×	50 2.1×	11 1.1×	6 1.2×	5 1.0×	8	170
Sylvain Lamprier France	6	62 0.7×	15 0.6×	13 1.3×	3 0.6×	4 0.8×	15	80
Zhanlin Sun China	3	65 0.8×	13 0.5×	7 0.7×	4 0.8×	8 1.6×	4	75
Sujith Ravi United States	7	90 1.1×	26 1.1×	21 2.1×	5 1.0×	9 1.8×	10	101
Ivana Balažević United Kingdom	3	86 1.0×	25 1.0×	10 1.0×	10 2.0×	2 0.4×	4	104
Itsumi Saito Japan	6	93 1.1×	27 1.1×	8 0.8×	4 0.8×	4 0.8×	14	102
Eva Schlinger United States	4	102 1.2×	31 1.3×	6 0.6×	6 1.2×	7 1.4×	5	104
Zhongyang Li China	6	85 1.0×	25 1.0×	13 1.3×	2 0.4×	16 3.2×	19	109

Countries citing papers authored by Stephen Mussmann

Since Specialization

Citations

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

Fields of papers citing papers by Stephen Mussmann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephen Mussmann

This figure shows the co-authorship network connecting the top 25 collaborators of Stephen Mussmann. A scholar is included among the top collaborators of Stephen Mussmann 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 Stephen Mussmann. Stephen Mussmann 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:

10 of 10 papers shown

1.

Chen, Yifang, Stephen Mussmann, Jeff Bilmes, et al.. (2024). An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. 6549–6560. 2 indexed citations

2.

Zhang, Enhao, Dong He, Stephen Mussmann, et al.. (2023). VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building. Proceedings of the VLDB Endowment. 16(13). 4188–4201. 2 indexed citations

3.

Coleman, Cody, Christopher Yeh, Stephen Mussmann, et al.. (2020). Selection via Proxy: Efficient Data Selection for Deep Learning. International Conference on Learning Representations. 6 indexed citations

4.

Mussmann, Stephen, Robin Jia, & Percy Liang. (2020). On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks. 3400–3413. 10 indexed citations

5.

Mussmann, Stephen & Percy Liang. (2018). On the Relationship between Data Efficiency and Error for Uncertainty Sampling. International Conference on Machine Learning. 3671–3679. 2 indexed citations

6.

Mussmann, Stephen & Percy Liang. (2018). Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss. arXiv (Cornell University). 31. 6955–6964. 4 indexed citations

7.

Chaganty, Arun Tejasvi, Stephen Mussmann, & Percy Liang. (2018). The price of debiasing automatic metrics in natural language evalaution. 50 indexed citations

8.

Mussmann, Stephen & Stefano Ermon. (2016). Learning and inference via maximum inner product search. International Conference on Machine Learning. 2587–2596. 13 indexed citations

9.

Mussmann, Stephen, et al.. (2015). Incorporating Assortativity and Degree Dependence into Scalable Network Models. Proceedings of the AAAI Conference on Artificial Intelligence. 29(1). 4 indexed citations

10.

Mussmann, Stephen, et al.. (2014). Assortativity in Chung Lu Random Graph Models. 1–8.

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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