Rocco A. Servedio

137 papers receiving 1.9k citations

Peers

Rocco A. Servedio
Comparison fields: 5 of 94
  • Artificial Intelligence 1.5k
  • Computational Theory and Mathematics 962
  • Computer Networks and Communications 402
  • Electrical and Electronic Engineering 274
  • Computer Vision and Pattern Recognition 124
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Citations per field
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Citations per year

Countries citing papers authored by Rocco A. Servedio

Since Specialization
Citations

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

Fields of papers citing papers by Rocco A. Servedio

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rocco A. Servedio. 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 Rocco A. Servedio. The network helps show where Rocco A. Servedio may publish in the future.

Co-authorship network of co-authors of Rocco A. Servedio

This figure shows the co-authorship network connecting the top 25 collaborators of Rocco A. Servedio. A scholar is included among the top collaborators of Rocco A. Servedio 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 Rocco A. Servedio. Rocco A. Servedio 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
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Learning sparse mixtures of permutations from noisy information
0
5 1
6
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7
Efficient average-case population recovery in the presence of insertions and deletions
2
8
Simple and efficient pseudorandom generators from Gaussian processes.
1
9
Testing probability distributions using conditional samples
19
10 2
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Consistency versus Realizable H-Consistency for Multiclass Classification
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12 15
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On a special case of rigidity.
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14
Algorithms and hardness results for parallel large margin learning
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Learning large-margin halfspaces with more malicious noise
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Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate
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Boosting the Area under the ROC Curve
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Testing for Concise Representations
31

About Rocco A. Servedio

Rocco A. Servedio is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 145 papers that have together received 2.1k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (109 papers), Complexity and Algorithms in Graphs (67 papers) and Algorithms and Data Compression (44 papers). The work is most often cited by research in Computational Theory and Mathematics (962 citations), Artificial Intelligence (1.5k citations) and Discrete Mathematics and Combinatorics (75 citations). Rocco A. Servedio has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Ryan O’Donnell, Adam R. Klivans, Philip M. Long, Ilias Diakonikolas, Jon Feldman, Adam Tauman Kalai, Tal Malkin, Steven J. Gortler, Yishay Mansour and Li-Yang Tan. Their work appears in journals such as IEEE Transactions on Information Theory, The Journal of Urology and Journal of the ACM.

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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