Garvesh Raskutti
- Statistics and Probability top 0.5%
- Artificial Intelligence top 2%
- Computational Mechanics top 2%
- Electrical and Electronic Engineering
- Molecular Biology
- Co-authors
- Martin J. WainwrightBin YuPradeep RavikumarCaroline UhlerKerry HintonRebecca WillettRodney S. TuckerPeter M. Farrell
- Topics
- Statistical Methods and Inference (21 papers)Sparse and Compressive Sensing Techniques (12 papers)Bayesian Modeling and Causal Inference (5 papers)
- Journals
- Journal of the American Statistical AssociationIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Information Theory
- Partner nations
- United StatesAustraliaHong Kong
In The Last Decade
Garvesh Raskutti
40 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 128
- Statistics and Probability 686
- Artificial Intelligence 667
- Computational Mechanics 447
- Electrical and Electronic Engineering 165
- Molecular Biology 160
Countries citing papers authored by Garvesh Raskutti
This map shows the geographic impact of Garvesh Raskutti'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 Garvesh Raskutti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Garvesh Raskutti more than expected).
Fields of papers citing papers by Garvesh Raskutti
This network shows the impact of papers produced by Garvesh Raskutti. 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 Garvesh Raskutti. The network helps show where Garvesh Raskutti may publish in the future.
Co-authorship network of co-authors of Garvesh Raskutti
This figure shows the co-authorship network connecting the top 25 collaborators of Garvesh Raskutti. A scholar is included among the top collaborators of Garvesh Raskutti 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 Garvesh Raskutti. Garvesh Raskutti is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 37 | |
| 2 | 19 | |
| 3 | Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes | 8 |
| 4 | 31 | |
| 5 | 2 | |
| 6 | 4 | |
| 7 | 16 | |
| 8 | 21 | |
| 9 | 6 | |
| 10 | 14 | |
| 11 | Learning large-scale Poisson DAG models based on overdispersion scoring | 8 |
| 12 | Statistical and Algorithmic Perspectives on Randomized Sketching for Ordinary Least-Squares | 7 |
| 13 | 111 | |
| 14 | 392 | |
| 15 | 189 | |
| 16 | Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness | 8 |
| 17 | 28 | |
| 18 | Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of boldmathell_1-regularized MLE | 55 |
| 19 | 52 | |
| 20 | 14 |
About Garvesh Raskutti
Garvesh Raskutti is a scholar working on Computational Mathematics, Statistics and Probability and Computational Mechanics, having authored 41 papers that have together received 1.8k indexed citations. Recurring topics across this work include Statistical Methods and Inference (21 papers), Sparse and Compressive Sensing Techniques (12 papers) and Bayesian Modeling and Causal Inference (5 papers). The work is most often cited by research in Statistics and Probability (686 citations), Computational Mathematics (35 citations) and Computational Mechanics (447 citations). Garvesh Raskutti has collaborated with scholars based in United States, Australia and Hong Kong. Frequent co-authors include Martin J. Wainwright, Bin Yu, Pradeep Ravikumar, Bin Yu, Caroline Uhler, Kerry Hinton, Rebecca Willett, Rodney S. Tucker, Bin Yu and Peter M. Farrell. Their work appears in journals such as Journal of the American Statistical Association, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Information 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.