Pradeep Ravikumar
Impact in
- Artificial Intelligence top 0.5%
- Semantic Web and Ontologies
- Topic Modeling
- Machine Learning and Algorithms
- Bayesian Modeling and Causal Inference
- Machine Learning and Data Classification
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- Data Quality and Management
Papers in
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- Machine Learning and Algorithms 25
- Bayesian Modeling and Causal Inference 23
- Bayesian Methods and Mixture Models 13
- Machine Learning and Data Classification 9
- Stochastic Gradient Optimization Techniques 8
- Domain Adaptation and Few-Shot Learning 8
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- Statistical Methods and Inference 31
- Co-authors
- William W. Cohen (3 shared papers)Stephen E. Fienberg (2 shared papers)Inderjit S. Dhillon (28 shared papers)Martin J. Wainwright (7 shared papers)Ambuj Tewari (5 shared papers)Nagarajan Natarajan (5 shared papers)John Lafferty (5 shared papers)Cho‐Jui Hsieh (9 shared papers)
- Journals
- Journal of Machine Learning Research (3 papers)IEEE Transactions on Information Theory (1 paper)IEEE Intelligent Systems (1 paper)Statistical Science (1 paper)PLoS ONE (1 paper)
- Partner nations
- United StatesIndiaTaiwan
In The Last Decade
Pradeep Ravikumar
103 papers receiving 4.3k citations
Pradeep Ravikumar's Hit Papers
Peers
Comparison fields: 5 of 143
- Artificial Intelligence 2.9k
- Management Science and Operations Research 1.0k
- Statistics and Probability 630
- Computational Mathematics 27
- Information Systems 891
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 108 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 | 1058 |
| 2 | 2003 | 366 | |
| 3 | Learning with Noisy Labels | 2013 | 275 |
| 4 | A Dirty Model for Multi-task Learning | 2010 | 239 |
| 5 | 2013 | 170 | |
| 6 | 2012 | 166 | |
| 7 | Collaborative filtering with graph information: consistency and scalable methods | 2015 | 120 |
| 8 | High-Dimensional Graphical Model Selection Using ell_1-Regularized Logistic Regression | 2006 | 119 |
| 9 | 2012 | 118 | |
| 10 | 2018 | 97 | |
| 11 | 2018 | 96 | |
| 12 | SpAM: Sparse Additive Models | 2007 | 95 |
| 13 | QUIC: quadratic approximation for sparse inverse covariance estimation | 2014 | 85 |
| 14 | BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables | 2013 | 80 |
| 15 | Greedy Algorithms for Structurally Constrained High Dimensional Problems | 2011 | 79 |
| 16 | 2004 | 76 | |
| 17 | Consistent Binary Classification with Generalized Performance Metrics | 2014 | 73 |
| 18 | PD-sparse: a primal and dual sparse approach to extreme multiclass and multilabel classification | 2016 | 73 |
| 19 | 2006 | 70 | |
| 20 | Graphical Models via Generalized Linear Models | 2012 | 65 |
About Pradeep Ravikumar
Pradeep Ravikumar is a scholar working on Artificial Intelligence, Statistics and Probability, Computational Mechanics, Molecular Biology and Management Science and Operations Research, having authored 108 papers that have together received 4.6k indexed citations. Recurring topics across this work include Statistical Methods and Inference (31 papers), Sparse and Compressive Sensing Techniques (27 papers), Machine Learning and Algorithms (25 papers), Bayesian Modeling and Causal Inference (23 papers), Bayesian Methods and Mixture Models (13 papers), Machine Learning and Data Classification (9 papers), Stochastic Gradient Optimization Techniques (8 papers) and Domain Adaptation and Few-Shot Learning (8 papers). The work is most often cited by research in Artificial Intelligence (2.9k citations), Management Science and Operations Research (1.0k citations), Statistics and Probability (630 citations), Computational Mathematics (27 citations) and Information Systems (891 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, Cho‐Jui Hsieh, Mátyás A. Sustik and Mikhail Bilenko. Their work appears in journals such as Journal of Machine Learning Research, IEEE Transactions on Information Theory, IEEE Intelligent Systems, Statistical Science and PLoS ONE.
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.