Mitchell Stern

2.4k total citations
11 papers, 520 citations indexed

About

Mitchell Stern is a scholar working on Artificial Intelligence, Management Science and Operations Research and Molecular Biology. According to data from OpenAlex, Mitchell Stern has authored 11 papers receiving a total of 520 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 2 papers in Management Science and Operations Research and 1 paper in Molecular Biology. Recurrent topics in Mitchell Stern's work include Topic Modeling (6 papers), Natural Language Processing Techniques (6 papers) and Speech and dialogue systems (2 papers). Mitchell Stern is often cited by papers focused on Topic Modeling (6 papers), Natural Language Processing Techniques (6 papers) and Speech and dialogue systems (2 papers). Mitchell Stern collaborates with scholars based in United States, Germany and Japan. Mitchell Stern's co-authors include Dan Klein, Maxim Rabinovich, Jacob Andreas, William Chan, Jamie Kiros, Jakob Uszkoreit, Dawn Song, Eric Wallace, Ashia Wilson and Benjamin Recht and has published in prestigious journals such as Chemical Communications, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Mitchell Stern

11 papers receiving 473 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mitchell Stern United States	8	438	133	106	42	37	11	520
Xiaochen Lian China	8	111 0.3×	196 1.5×	39 0.4×	49 1.2×	23 0.6×	13	331
Jinjing Zhou China	4	284 0.6×	161 1.2×	47 0.4×	11 0.3×	5 0.1×	5	391
Gregory V. Bard United States	5	142 0.3×	51 0.4×	49 0.5×	34 0.8×	8 0.2×	9	210
Lingfan Yu China	4	188 0.4×	114 0.9×	40 0.4×	13 0.3×	5 0.1×	6	316
J. Austin United Kingdom	8	124 0.3×	106 0.8×	36 0.3×	52 1.2×	3 0.1×	35	258
Yiwei Wang Singapore	7	203 0.5×	46 0.3×	50 0.5×	15 0.4×	3 0.1×	14	262
David Lugato France	7	108 0.2×	65 0.5×	44 0.4×	8 0.2×	56 1.5×	15	325
Ravi Teja Mullapudi United States	7	110 0.3×	192 1.4×	25 0.2×	6 0.1×	22 0.6×	10	386
Jiahao Liu China	11	218 0.5×	51 0.4×	62 0.6×	54 1.3×	23 0.6×	41	370
Kush Bhatia United States	6	279 0.6×	78 0.6×	44 0.4×	13 0.3×	1 0.0×	12	341

Countries citing papers authored by Mitchell Stern

Since Specialization

Citations

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

Fields of papers citing papers by Mitchell Stern

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mitchell Stern

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

All Works

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11 of 11 papers shown

1.

Ting, Jeffrey, Teresa Tamayo-Mendoza, Shannon R. Petersen, et al.. (2023). Frontiers in nonviral delivery of small molecule and genetic drugs, driven by polymer chemistry and machine learning for materials informatics. Chemical Communications. 59(96). 14197–14209. 6 indexed citations

2.

Wallace, Eric, Mitchell Stern, & Dawn Song. (2020). Imitation Attacks and Defenses for Black-box Machine Translation Systems. 5531–5546. 43 indexed citations

3.

Chan, William, Mitchell Stern, Jamie Kiros, & Jakob Uszkoreit. (2020). An Empirical Study of Generation Order for Machine Translation. 5764–5773. 2 indexed citations

4.

Stern, Mitchell, William Chan, Jamie Kiros, & Jakob Uszkoreit. (2019). Insertion Transformer: Flexible Sequence Generation via Insertion Operations. arXiv (Cornell University). 5976–5985. 60 indexed citations

5.

Tripuraneni, Nilesh, Mitchell Stern, Chi Jin, Jeffrey Regier, & Michael I. Jordan. (2018). Stochastic Cubic Regularization for Fast Nonconvex Optimization. Neural Information Processing Systems. 31. 2899–2908. 20 indexed citations

6.

Gaddy, David, Mitchell Stern, & Dan Klein. (2018). What’s Going On in Neural Constituency Parsers? An Analysis. 999–1010. 36 indexed citations

7.

Chen, Jianbo, Mitchell Stern, Martin J. Wainwright, & Michael I. Jordan. (2017). Kernel feature selection via conditional covariance minimization. Neural Information Processing Systems. 30. 6949–6958. 7 indexed citations

8.

Wilson, Ashia, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, & Benjamin Recht. (2017). The Marginal Value of Adaptive Gradient Methods in Machine Learning. Neural Information Processing Systems. 30. 4148–4158. 65 indexed citations

9.

Rabinovich, Maxim, Mitchell Stern, & Dan Klein. (2017). Abstract Syntax Networks for Code Generation and Semantic Parsing. 1139–1149. 159 indexed citations

10.

Stern, Mitchell, Jacob Andreas, & Dan Klein. (2017). A Minimal Span-Based Neural Constituency Parser. 93 indexed citations

11.

Stern, Mitchell, Daniel Fried, & Dan Klein. (2017). Effective Inference for Generative Neural Parsing. 1695–1700. 29 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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