Anne-Laure Boulesteix

Artificial Intelligence · 2006–2024

A researcher in the Science corpus: 15 papers; trusts 95 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 185 citations into it; #4,785 of 35,345 by trust here, #7,531 by citations
• Statistics and Probability — 98 citations into it; #3,024 of 12,932 by trust here, #1,860 by citations
• Molecular Biology — 65 citations into it; #1,846 of 17,773 by trust here, #4,775 by citations
• Computational Theory and Mathematics — 50 citations into it; #5,758 of 12,182 by trust here, #5,465 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Optimization & Training — 40 citations into it
• Statistical Learning Theory — 33 citations into it; #595 of 1,038 by trust here, #770 by citations
• Evaluation & Benchmarks — 28 citations into it

Whom Anne-Laure Boulesteix trusts overall

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

1. Jerome H. Friedman
2. David Hilton Wolpert
3. Bernd Bischl
4. Frank Hutter
5. Trevor J. Hastie
6. Torsten Hothorn
7. Leo Breiman
8. Robert Tibshirani
9. Simon N. Wood
10. Andy Liaw

Who trusts Anne-Laure Boulesteix overall

1. Davide Chicco
2. Evangelia Christodoulou
3. Gilles Louppe
4. Hristos Tyralis
5. Philipp Probst
6. R. Couronné
7. Kim-Anh Lê Cao
8. Jie Ma
9. Marco Sandri
10. Bai Jiang

Papers from Anne-Laure Boulesteix's perspective

Ordered by how strongly the researchers Anne-Laure Boulesteix trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Random Forests
9. Long Short-Term Memory
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Unbiased split selection for classification trees based on the Gini Index (2007) — 319 citations
• Bias in random forest variable importance measures: Illustrations, sources and a solution (2007) — 1,433 citations
• Partial least squares: a versatile tool for the analysis of high-dimensional genomic data (2006) — 830 citations
• Tunability: Importance of Hyperparameters of Machine Learning Algorithms (2018) — 816 citations
• A Plea for Neutral Comparison Studies in Computational Sciences (2012) — 134 citations
• Penalized Partial Least Squares with Applications to B-Spline Transformations and Functional Data (2006) — 81 citations
• Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics (2012) — 896 citations
• Hyperparameters and tuning strategies for random forest (2018) — 2,058 citations
• NetCoMi: network construction and comparison for microbiome data in R (2020) — 443 citations
• TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods (2024) — 2,500 citations
• Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges (2021) — 1,013 citations
• Random forest versus logistic regression: a large-scale benchmark experiment (2018) — 785 citations
• To tune or not to tune the number of trees in random forest? (2017) — 460 citations
• The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models (2016) — 6 citations
• Ten Simple Rules for Reducing Overoptimistic Reporting in Methodological Computational Research (2015) — 59 citations