Aaditya Ramdas

3.1k citations
77 papers · 1.0k indexed · 1 hit paper · h-index 16

Impact in

Papers in

Aaditya Ramdas

73 papers receiving 988 citations

Hit Papers

Conformal prediction beyond exchangeability 2023 · 76 citations
762023202620242025255075

Peers

Aaditya Ramdas
Comparison fields: 5 of 119
  • Statistics and Probability 381
  • Computational Mathematics 12
  • Artificial Intelligence 414
  • Statistics, Probability and Uncertainty 92
  • Computational Mechanics 162
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Citations per field
00.5×10×14.8×
Onureena Banerjee · 1×
Citations per year

Countries citing papers authored by Aaditya Ramdas

Since Specialization
Citations

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

Fields of papers citing papers by Aaditya Ramdas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Aaditya Ramdas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Aaditya Ramdas Line = papers co-authored together Aaditya Ramdas links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
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Asymptotic behavior of $\ell_p$-based Laplacian regularization in semi-supervised learning
201610
19
Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions.
20144
20
Optimal Stochastic Convex Optimization Through The Lens Of Active Learning
20120

About Aaditya Ramdas

Aaditya Ramdas is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty, Management Science and Operations Research, Artificial Intelligence and General Decision Sciences, having authored 77 papers that have together received 1.0k indexed citations. Recurring topics across this work include Statistical Methods and Inference (31 papers), Statistical Methods in Clinical Trials (26 papers), Machine Learning and Algorithms (16 papers), Advanced Statistical Process Monitoring (12 papers), Statistical Methods and Bayesian Inference (12 papers), Advanced Bandit Algorithms Research (12 papers), Advanced Statistical Methods and Models (11 papers) and Sparse and Compressive Sensing Techniques (6 papers). The work is most often cited by research in Statistics and Probability (381 citations), Computational Mathematics (12 citations), Artificial Intelligence (414 citations), Statistics, Probability and Uncertainty (92 citations) and Computational Mechanics (162 citations). Aaditya Ramdas has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Deanna Needell, Ryan J. Tibshirani, Anna Ma, Rina Foygel Barber, Leila Wehbe, Brian Murphy, Alona Fyshe, Partha Talukdar, Ruodu Wang and Tom M. Mitchell. Their work appears in journals such as Biometrika, The Annals of Statistics, Bernoulli, Journal of the Royal Statistical Society Series B (Statistical Methodology) and Electronic Journal of Statistics.

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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