Tushar Khot

46 papers receiving 1.3k citations

Hit Papers

Can a Suit of Armor Conduct Electricity? A New Dataset fo...2018202620202023201850100150200250

Peers

Tushar Khot
Comparison fields: 5 of 79
  • Artificial Intelligence 1.2k
  • Computer Vision and Pattern Recognition 342
  • Information Systems 181
  • Molecular Biology 63
  • Management Science and Operations Research 56
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Grzegorz Kondrak Canada
Chandra Bhagavatula United States
Andrew M. Dai United States
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Adam Fisch United States
Barry Haddow United Kingdom
Saïd Ouatik El Alaoui Morocco
Tom Kwiatkowski United States
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Patrick Lewis United Kingdom
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Citations per year

Countries citing papers authored by Tushar Khot

Since Specialization
Citations

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

Fields of papers citing papers by Tushar Khot

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tushar Khot

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 9
3 0
4 91
5 22
6 2
7 64
8 2
9
Natural Perturbation for Robust Question Answering
2
10
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringbreakdown →
284
11 31
12 20
13
Question answering via integer programming over semi-structured knowledge
12
14 9
15 7
16
Anomaly Detection in Text: The Value of Domain Knowledge.
7
17 9
18 13
19
Boosting relational dependency networks
6
20 16

About Tushar Khot

Tushar Khot is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Toxicology, having authored 48 papers that have together received 1.4k indexed citations. Recurring topics across this work include Topic Modeling (25 papers), Natural Language Processing Techniques (25 papers) and Bayesian Modeling and Causal Inference (12 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Computer Vision and Pattern Recognition (342 citations) and Health Informatics (21 citations). Tushar Khot has collaborated with scholars based in United States, Germany and Belgium. Frequent co-authors include Ashish Sabharwal, Peter E. Clark, Todor Mihaylov, Niranjan Balasubramanian, Sriraam Natarajan, Harsh Trivedi, Daniel Khashabi, Kristian Kersting, Jude Shavlik and Dan Roth. Their work appears in journals such as Machine Learning, Lecture notes in computer science and Knowledge and Information Systems.

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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