Mark Yatskar

5.9k total citations · 2 hit papers
23 papers, 1.6k citations indexed

About

Mark Yatskar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Science Applications. According to data from OpenAlex, Mark Yatskar has authored 23 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 12 papers in Computer Vision and Pattern Recognition and 2 papers in Computer Science Applications. Recurrent topics in Mark Yatskar's work include Topic Modeling (14 papers), Multimodal Machine Learning Applications (12 papers) and Natural Language Processing Techniques (8 papers). Mark Yatskar is often cited by papers focused on Topic Modeling (14 papers), Multimodal Machine Learning Applications (12 papers) and Natural Language Processing Techniques (8 papers). Mark Yatskar collaborates with scholars based in United States, South Korea and United Kingdom. Mark Yatskar's co-authors include Luke Zettlemoyer, Kai-Wei Chang, Vicente Ordóñez, Tianlu Wang, Jieyu Zhao, Christopher Clark, Ali Farhadi, Yejin Choi, Eunsol Choi and Wen-tau Yih and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, arXiv (Cornell University) and PubMed.

In The Last Decade

Mark Yatskar

22 papers receiving 1.5k 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.

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

2017 403 citations Jieyu Zhao, Tianlu Wang et al. profile →
QuAC: Question Answering in Context

2018 343 citations Eunsol Choi, He He et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark Yatskar United States	12	1.3k	663	153	93	67	23	1.6k
Jieyu Zhao China	14	804 0.6×	328 0.5×	178 1.2×	65 0.7×	82 1.2×	95	1.3k
Tolga Bolukbasi United States	6	810 0.6×	217 0.3×	217 1.4×	72 0.8×	123 1.8×	15	1.2k
Mennatallah El‐Assady Germany	17	561 0.4×	582 0.9×	51 0.3×	77 0.8×	79 1.2×	92	1.1k
Anders Søgaard Denmark	27	2.6k 2.0×	402 0.6×	52 0.3×	224 2.4×	83 1.2×	203	2.9k
Niket Tandon United States	17	721 0.6×	546 0.8×	32 0.2×	103 1.1×	53 0.8×	51	1.2k
Kuntal Dey India	15	638 0.5×	141 0.2×	311 2.0×	175 1.9×	78 1.2×	54	1.1k
Tongshuang Wu United States	17	608 0.5×	208 0.3×	93 0.6×	133 1.4×	55 0.8×	48	971
Danding Wang China	8	698 0.6×	120 0.2×	290 1.9×	151 1.6×	146 2.2×	18	1.0k
Manuel Montes-y-Gómez Mexico	22	1.3k 1.0×	199 0.3×	62 0.4×	450 4.8×	167 2.5×	126	1.7k
Barbara Plank Denmark	26	1.9k 1.5×	352 0.5×	18 0.1×	235 2.5×	149 2.2×	145	2.2k

Countries citing papers authored by Mark Yatskar

Since Specialization

Citations

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

Fields of papers citing papers by Mark Yatskar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Yatskar

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

20 of 20 papers shown

1.

Yang, Yue, Ajay Patel, Matt Deitke, et al.. (2025). Scaling Text-Rich Image Understanding via Code-Guided Synthetic Multimodal Data Generation. 17486–17505. 1 indexed citations

2.

Malaviya, Chaitanya, Joseph Chee Chang, Dan Roth, et al.. (2025). Contextualized Evaluations: Judging Language Model Responses to Underspecified Queries. Transactions of the Association for Computational Linguistics. 13. 878–900.

3.

Malaviya, Chaitanya, et al.. (2024). ExpertQA: Expert-Curated Questions and Attributed Answers. 3025–3045. 8 indexed citations

4.

Malaviya, Chaitanya, et al.. (2024). What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception. 3046–3065. 1 indexed citations

5.

Yang, Yue, Fan-Yun Sun, Luca Weihs, et al.. (2024). Holodeck: Language Guided Generation of 3D Embodied AI Environments. 16277–16287. 10 indexed citations

6.

Callison-Burch, Chris, Mona A. Gandhi, James C. Gee, et al.. (2024). A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis. PubMed. 37. 90683–90713. 1 indexed citations

7.

Malaviya, Chaitanya, et al.. (2023). AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous Coreference. 1023–1030. 1 indexed citations

8.

Yang, Yue, et al.. (2023). Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification. 19187–19197. 56 indexed citations

9.

Malaviya, Chaitanya, et al.. (2022). Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models. 6525–6540. 1 indexed citations

10.

Clark, Christopher, Jordi Salvador, Dustin Schwenk, et al.. (2021). Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 1864–1886. 1 indexed citations

11.

Li, Liunian Harold, Mark Yatskar, Da Yin, Cho‐Jui Hsieh, & Kai-Wei Chang. (2020). What Does BERT with Vision Look At?. 5265–5275. 73 indexed citations

12.

Clark, Christopher, Mark Yatskar, & Luke Zettlemoyer. (2019). Don’t Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases. 4067–4080. 199 indexed citations

13.

Yatskar, Mark. (2019). A Qualitative Comparison of. 2318–2323. 26 indexed citations

14.

Wang, Tianlu, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, & Vicente Ordóñez. (2019). Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations. 5309–5318. 179 indexed citations

15.

Choi, Eunsol, He He, Mohit Iyyer, et al.. (2018). QuAC: Question Answering in Context. 2174–2184. 343 indexed citations breakdown →

16.

Wang, Tianlu, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, & Vicente Ordóñez. (2018). Adversarial Removal of Gender from Deep Image Representations.. 7 indexed citations

17.

Zhao, Jieyu, Tianlu Wang, Mark Yatskar, Vicente Ordóñez, & Kai-Wei Chang. (2017). Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints. 2979–2989. 403 indexed citations breakdown →

18.

Yatskar, Mark, Luke Zettlemoyer, & Ali Farhadi. (2016). Situation Recognition: Visual Semantic Role Labeling for Image Understanding. 5534–5542. 132 indexed citations

19.

Yatskar, Mark, Michel Galley, Lucy Vanderwende, & Luke Zettlemoyer. (2014). See No Evil, Say No Evil: Description Generation from Densely Labeled Images. 110–120. 33 indexed citations

20.

Yatskar, Mark, Bo Pang, Cristian Danescu-Niculescu-Mizil, & Lillian Lee. (2010). For the sake of simplicity: Unsupervised extraction of lexical simplifications from Wikipedia. arXiv (Cornell University). 365–368. 93 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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