Ricardo P. Masini

Statistics and Probability · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 68 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 53 citations into it
• Economics and Econometrics — 35 citations into it; #18,989 of 23,481 by trust here, #22,128 by citations
• Artificial Intelligence — 18 citations into it
• Finance — 18 citations into it
• Management Science and Operations Research — 14 citations into it
• Causal Inference & Discovery — 12 citations into it
• Statistical Learning Theory — 12 citations into it
• Control and Systems Engineering — 9 citations into it

Whom Ricardo P. Masini trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Hui Zou
2. Robert Tibshirani
3. Jianqing Fan
4. Trevor J. Hastie
5. Francis X. Diebold
6. Leo Breiman
7. Xiaohong Chen
8. David John Cameron MacKay
9. Jerome H. Friedman
10. Fulvio Corsi

Who trusts Ricardo P. Masini overall

1. Bruno Ferman

Papers from Ricardo P. Masini's perspective

Ordered by how strongly the researchers Ricardo P. Masini trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Shrinkage and Selection via the Lasso
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Addendum: Regularization and variable selection via the elastic net
6. Model selection and estimation in regression with grouped variables
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. Asymptotics for lasso-type estimators
10. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

Their papers

• ArCo: An artificial counterfactual approach for high-dimensional panel time-series data (2018) — 74 citations
• Machine Learning Advances for Time Series Forecasting (2020) — 468 citations