Andreas Buja

Statistics and Probability · 2005–2020

A researcher in the Science corpus: 15 papers; trusts 37 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 118 citations into it; #3,749 of 12,932 by trust here, #1,457 by citations
• Artificial Intelligence — 31 citations into it
• Genetics — 21 citations into it
• Economics and Econometrics — 15 citations into it
• Molecular Biology — 15 citations into it
• Control and Systems Engineering — 11 citations into it
• Statistical Learning Theory — 11 citations into it
• Computational Mechanics — 9 citations into it

Whom Andreas Buja trusts overall

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

1. Halbert White
2. Leo Breiman
3. Jerry A. Hausman
4. Richard A. Berk
5. W. Hoeffding
6. Peter Buhlmann
7. David Freedman
8. Lars Peter Hansen
9. Tong Zhang
10. K. Liang

Who trusts Andreas Buja overall

1. Balaji Lakshminarayanan
2. Bradley Efron
3. Gérard Biau
4. Tilmann Gneiting
5. Richard A. Berk
6. Qingyuan Zhao
7. Stefan Wager
8. Robert C. Williamson
9. Mark D. Reid
10. Gonzalo Martínez-Muñoz

Papers from Andreas Buja's perspective

Ordered by how strongly the researchers Andreas Buja 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. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Valid post-selection inference (2013) — 651 citations
• Models as Approximations: How Random Predictors and Model Violations Invalidate Classical Inference in Regression (2014) — 33 citations
• Statistical inference for exploratory data analysis and model diagnostics (2009) — 170 citations
• Models as Approximations I: Consequences Illustrated with Linear Regression (2014) — 101 citations
• A Sparse Singular Value Decomposition Method for High-Dimensional Data (2014) — 43 citations
• Models as Approximations II: A Model-Free Theory of Parametric Regression (2016) — 55 citations
• Working with Misspecified Regression Models (2018) — 11 citations
• Valid post-selection inference in model-free linear regression (2020) — 30 citations
• Assumption Lean Regression (2018) — 28 citations
• Models as Approximations - A Conspiracy of Random Regressors and Model Deviations Against Classical Inference in Regression (2015) — 24 citations
• Rare de novo and transmitted copy-number variation in autistic spectrum disorders. (2011) — 750 citations
• Graphical inference for infovis (2010) — 136 citations
• Data Visualization With Multidimensional Scaling (2008) — 336 citations
• OBSERVATIONS ON BAGGING (2006) — 104 citations
• Loss Functions for Binary Class Probability Estimation and Classification: Structure and Applications (2005) — 321 citations