Sumanta Basu
Molecular Biology · 2013–2020
A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 49 citations into it; #12,805 of 17,773 by trust here, #13,891 by citations
- Statistics and Probability — 38 citations into it; #5,249 of 12,932 by trust here, #4,132 by citations
- Artificial Intelligence — 22 citations into it; #29,272 of 35,345 by trust here, #23,731 by citations
- Statistical Learning Theory — 12 citations into it; #955 of 1,038 by trust here, #817 by citations
- Finance — 10 citations into it
- Computational Mechanics — 9 citations into it; #4,353 of 10,001 by trust here, #5,813 by citations
- Economics and Econometrics — 9 citations into it
- Optimization & Training — 7 citations into it
Whom Sumanta Basu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Nicolai Meinshausen
- Helmut Ltkepohl
- Leo Breiman
- Jerome H. Friedman
- Trevor J. Hastie
- Andy Liaw
- Rajen Dinesh Shah
- Jian Zhou
- Paul Doukhan
- Peter J. Bickel
Who trusts Sumanta Basu overall
- Bin Yu
- Ali Shojaie
- Zeng Li
- Niklas Pfister
- Alex Tank
- Emily B. Fox
- Rina Friedberg
- W. James Murdoch
- Clifford Lam
- Stefan Bauer
Papers from Sumanta Basu's perspective
Ordered by how strongly the researchers Sumanta Basu trusts endorse each paper. Same corpus, a different order for every researcher.
- Random Forests
- Bagging Predictors
- Regression Shrinkage and Selection via the Lasso
- The Elements of Statistical Learning
- Model selection and estimation in regression with grouped variables
- Developmental roles of 21 Drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- On the conditions used to prove oracle results for the Lasso
- ImageNet classification with deep convolutional neural networks
- Controlling the false discovery rate: a practical and powerful approach to multiple testing
Their papers
- Iterative random forests to discover predictive and stable high-order interactions (2017) — 324 citations
- Regularized estimation in sparse high-dimensional time series models (2013) — 473 citations
- Random Forests for dependent data (2020) — 9 citations