Laurent Callot

Artificial Intelligence · 2012–2020

A researcher in the Science corpus: 7 papers; trusts 92 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it
• Statistics and Probability — 69 citations into it; #5,846 of 12,932 by trust here, #4,809 by citations
• Management Science and Operations Research — 56 citations into it
• Signal Processing — 54 citations into it
• Time-Series & Forecasting — 41 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it
• Economics and Econometrics — 36 citations into it; #17,708 of 23,481 by trust here, #18,597 by citations
• Finance — 34 citations into it

Whom Laurent Callot trusts overall

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

1. Jianqing Fan
2. Hui Zou
3. Robert Tibshirani
4. James H. Stock
5. Ming Yuan
6. Jerome H. Friedman
7. Anders Bredahl Kock
8. Trevor J. Hastie
9. Olivier Ledoit
10. Peter Buhlmann

Who trusts Laurent Callot overall

1. Anders Bredahl Kock
2. Ricardo P. Masini
3. David Salinas
4. P. R. Hansen
5. Marcelo C. Medeiros
6. Mehmet Caner
7. Timo Teräsvirta
8. Konstantinos Benidis
9. Michael Bohlke-Schneider

Papers from Laurent Callot's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Model selection and estimation in regression with grouped variables
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. The Adaptive Lasso and Its Oracle Properties

Their papers

• High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019) — 278 citations
• Oracle Inequalities for High Dimensional Vector Autoregressions (2012) — 247 citations
• Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice (2017) — 94 citations
• Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector Autoregressions (2012) — 31 citations
• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020) — 391 citations
• A Nodewise Regression Approach to Estimating Large Portfolios (2016) — 55 citations
• Deep Learning for Forecasting (2020)