Paul Valiant

30 papers receiving 490 citations

Peers

Paul Valiant
Comparison fields: 5 of 62
  • Artificial Intelligence 319
  • Computational Theory and Mathematics 116
  • Computer Networks and Communications 104
  • Management Science and Operations Research 97
  • Statistics and Probability 93
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Countries citing papers authored by Paul Valiant

Since Specialization
Citations

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

Fields of papers citing papers by Paul Valiant

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Valiant

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Valiant. A scholar is included among the top collaborators of Paul Valiant 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 Paul Valiant. Paul Valiant 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 26
2 30
3 30
4 1
5
Estimating the Unseen: Improved Estimators for Entropy and other Properties
39
6
Instance-by-instance optimal identity testing.
1
7 2
8 1
9
Distribution Free Evolvability of Polynomial Functions over all Convex Loss Functions.
1
10 118
11
Robustly Leveraging Collusion in Combinatorial Auctions
8
12
Testing monotonicity of distributions over general partial orders.
3
13
Estimating the unseen: A sublinear-sample canonical estimator of distributions.
15
14
A CLT and tight lower bounds for estimating entropy.
33
15 1
16 14
17
Collusion-Resilient Revenue In Combinatorial Auctions
1
18
Revenue in Truly Combinatorial Auctions and Adversarial Mechanism Design
4
19 1
20 3

About Paul Valiant

Paul Valiant is a scholar working on Discrete Mathematics and Combinatorics, Computational Theory and Mathematics and Management Science and Operations Research, having authored 30 papers that have together received 527 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (13 papers), Complexity and Algorithms in Graphs (9 papers) and Auction Theory and Applications (6 papers). The work is most often cited by research in Statistics and Probability (93 citations), Artificial Intelligence (319 citations) and Management Science and Operations Research (97 citations). Paul Valiant has collaborated with scholars based in United States, Netherlands and Israel. Frequent co-authors include Gregory Valiant, Daniel M. Kane, Hari Balakrishnan, Mythili Vutukuru, Swastik Kopparty, Georg Gottlob, Silvio Micali, Shang‐Hua Teng, Xi Chen and Monkol Lek. Their work appears in journals such as Nature Communications, Journal of the ACM and SIAM Journal on Computing.

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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