Gautam Kamath
-
- Privacy-Preserving Technologies in Data 9
- Cryptography and Data Security 8
- Machine Learning and Algorithms 8
- Adversarial Robustness in Machine Learning 3
-
- Statistical Methods and Inference 3
-
- Mobile Crowdsensing and Crowdsourcing 1
-
- Complexity and Algorithms in Graphs 2
-
- Advanced biosensing and bioanalysis techniques 1
- Co-authors
- Constantinos DaskalakisJayadev AcharyaNicole ImmorlicaRobert KleinbergClément L. CanonneJerry LiIlias DiakonikolasAlistair Stewart
- Journals
- Communications of the ACM (1 paper)Theory of Computing (1 paper)Statistica Sinica (1 paper)
- Partner nations
- United StatesCanadaAustralia
In The Last Decade
Gautam Kamath
18 papers receiving 110 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 81
- Health Informatics 2
- Statistics and Probability 12
- Statistical and Nonlinear Physics 14
- Computer Science Applications 6
Countries citing papers authored by Gautam Kamath
This map shows the geographic impact of Gautam Kamath'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 Gautam Kamath with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gautam Kamath more than expected).
Fields of papers citing papers by Gautam Kamath
This network shows the impact of papers produced by Gautam Kamath. 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 Gautam Kamath. The network helps show where Gautam Kamath may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gautam Kamath, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 13 | |
| 5 | 2023 | 3 | |
| 6 | 2022 | 7 | |
| 7 | 2022 | 9 | |
| 8 | 2022 | 2 | |
| 9 | 2021 | 2 | |
| 10 | Private Mean Estimation of Heavy-Tailed Distributions | 2020 | 0 |
| 11 | 2020 | 1 | |
| 12 | Actively Avoiding Nonsense in Generative Models | 2018 | 1 |
| 13 | Sever: A Robust Meta-Algorithm for Stochastic Optimization | 2018 | 17 |
| 14 | 2018 | 1 | |
| 15 | Optimal testing for properties of distributions | 2015 | 11 |
| 16 | 2015 | 3 | |
| 17 | 2015 | 6 | |
| 18 | Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians | 2014 | 9 |
| 19 | 2012 | 23 |
About Gautam Kamath
Gautam Kamath is a scholar working on Health Informatics, Artificial Intelligence and Statistics and Probability, having authored 19 papers that have together received 112 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (9 papers), Cryptography and Data Security (8 papers), Machine Learning and Algorithms (8 papers), Statistical Methods and Inference (3 papers), Adversarial Robustness in Machine Learning (3 papers), Complexity and Algorithms in Graphs (2 papers), Advanced biosensing and bioanalysis techniques (1 paper) and Mobile Crowdsensing and Crowdsourcing (1 paper). The work is most often cited by research in Artificial Intelligence (81 citations), Health Informatics (2 citations) and Statistics and Probability (12 citations). Gautam Kamath has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include Constantinos Daskalakis, Jayadev Acharya, Nicole Immorlica, Robert Kleinberg, Clément L. Canonne, Jerry Li, Ilias Diakonikolas, Alistair Stewart, Jacob Steinhardt and Daniel M. Kane. Their work appears in journals such as Communications of the ACM, Theory of Computing, Statistica Sinica, SHILAP Revista de lepidopterología and Conference on Learning Theory.
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.