Pradeep Ravikumar
Impact in
- Artificial Intelligence top 0.5%
- Semantic Web and Ontologies
- Topic Modeling
- Machine Learning and Algorithms
- Machine Learning and Data Classification
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- Data Quality and Management
Papers in
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- Statistical Methods and Inference 29
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- Machine Learning and Algorithms 21
- Bayesian Modeling and Causal Inference 21
- Bayesian Methods and Mixture Models 14
- Machine Learning and Data Classification 8
- Topic Modeling 8
- Domain Adaptation and Few-Shot Learning 6
- Co-authors
- William W. Cohen (3 shared papers)Stephen E. Fienberg (2 shared papers)Inderjit S. Dhillon (26 shared papers)Martin J. Wainwright (6 shared papers)Ambuj Tewari (5 shared papers)Nagarajan Natarajan (5 shared papers)John Lafferty (5 shared papers)Mikhail Bilenko (1 shared paper)
- Journals
- Journal of Machine Learning Research (3 papers)PLoS ONE (1 paper)The Annals of Statistics (1 paper)Statistical Science (1 paper)Journal of the Royal Statistical Society Series B (Statistical Methodology) (1 paper)
- Partner nations
- United StatesIndiaTaiwan
In The Last Decade
Pradeep Ravikumar
95 papers receiving 4.0k citations
Pradeep Ravikumar's Hit Papers
Peers
Comparison fields: 5 of 143
- Artificial Intelligence 2.7k
- Management Science and Operations Research 973
- Statistics and Probability 542
- Computational Mathematics 26
- Information Systems 883
Countries citing papers authored by Pradeep Ravikumar
This map shows the geographic impact of Pradeep Ravikumar'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 Pradeep Ravikumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pradeep Ravikumar more than expected).
Fields of papers citing papers by Pradeep Ravikumar
This network shows the impact of papers produced by Pradeep Ravikumar. 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 Pradeep Ravikumar. The network helps show where Pradeep Ravikumar may publish in the future.
Co-authors
The 25 scholars most cited alongside Pradeep Ravikumar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 102 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | A comparison of string distance metrics for name-matching tasks Hit paper breakdown → | 2003 | 1044 |
| 2 | 2003 | 362 | |
| 3 | Learning with Noisy Labels | 2013 | 293 |
| 4 | A Dirty Model for Multi-task Learning | 2010 | 243 |
| 5 | 2012 | 154 | |
| 6 | Collaborative filtering with graph information: consistency and scalable methods | 2015 | 124 |
| 7 | 2012 | 122 | |
| 8 | High-Dimensional Graphical Model Selection Using ell_1-Regularized Logistic Regression | 2006 | 117 |
| 9 | 2018 | 98 | |
| 10 | SpAM: Sparse Additive Models | 2007 | 95 |
| 11 | QUIC: quadratic approximation for sparse inverse covariance estimation | 2014 | 88 |
| 12 | Greedy Algorithms for Structurally Constrained High Dimensional Problems | 2011 | 82 |
| 13 | BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables | 2013 | 81 |
| 14 | PD-sparse: a primal and dual sparse approach to extreme multiclass and multilabel classification | 2016 | 79 |
| 15 | 2004 | 73 | |
| 16 | Consistent Binary Classification with Generalized Performance Metrics | 2014 | 73 |
| 17 | Graphical Models via Generalized Linear Models | 2012 | 69 |
| 18 | 2006 | 67 | |
| 19 | Graphical Models via Univariate Exponential Family Distributions. | 2015 | 66 |
| 20 | 2018 | 65 |
About Pradeep Ravikumar
Pradeep Ravikumar is a scholar working on Statistics and Probability, Artificial Intelligence, Computational Mechanics, Numerical Analysis and Management Science and Operations Research, having authored 102 papers that have together received 4.2k indexed citations. Recurring topics across this work include Statistical Methods and Inference (29 papers), Sparse and Compressive Sensing Techniques (23 papers), Machine Learning and Algorithms (21 papers), Bayesian Modeling and Causal Inference (21 papers), Bayesian Methods and Mixture Models (14 papers), Machine Learning and Data Classification (8 papers), Topic Modeling (8 papers) and Domain Adaptation and Few-Shot Learning (6 papers). The work is most often cited by research in Artificial Intelligence (2.7k citations), Management Science and Operations Research (973 citations), Statistics and Probability (542 citations), Computational Mathematics (26 citations) and Information Systems (883 citations). Pradeep Ravikumar has collaborated with scholars based in United States, India and Taiwan. Frequent co-authors include William W. Cohen, Stephen E. Fienberg, Inderjit S. Dhillon, Martin J. Wainwright, Ambuj Tewari, Nagarajan Natarajan, John Lafferty, Mikhail Bilenko, Eunho Yang and Sujay Sanghavi. Their work appears in journals such as Journal of Machine Learning Research, PLoS ONE, The Annals of Statistics, Statistical Science and Journal of the Royal Statistical Society Series B (Statistical Methodology).
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.