Michael Kohler

Statistics and Probability · 2000–2019

A researcher in the Science corpus: 7 papers; trusts 13 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 23 citations into it; #1,235 of 12,932 by trust here, #628 by citations
• Statistical Learning Theory — 15 citations into it
• Artificial Intelligence — 7 citations into it; #19,955 of 35,345 by trust here, #27,551 by citations
• Model Architectures — 5 citations into it

Whom Michael Kohler trusts overall

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

1. C. Stone
2. David Haussler
3. László Györfi
4. Tomas Sauer
5. David E. Rumelhart
6. Vladimir Naumovich Vapnik
7. Andrew R. Barron
8. Luc Devroye
9. Yuhai Wu
10. Francis A. Longstaff

Who trusts Michael Kohler overall

1. Csaba Szepesvari
2. Amir-massoud Farahmand
3. Johannes Schmidt-Hieber
4. Vladimir Koltchinskii
5. R´emi Munos
6. Gérard Biau
7. Samory Kpotufe
8. Y. Baraud
9. Dmitry Panchenko
10. Lauren Hannah

Papers from Michael Kohler's perspective

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

1. Spline Models for Observational Data
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Learning internal representations by error propagation
6. Projection Pursuit Regression
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Optimal Global Rates of Convergence for Nonparametric Regression
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Distribution-Free Theory of Nonparametric Regression (2002) — 2,320 citations
• Rates of convergence for partitioning and nearest neighbor regression estimates with unbounded data (2006) — 55 citations
• Inequalities for uniform deviations of averages from expectations with applications to nonparametric regression (2000) — 55 citations
• On deep learning as a remedy for the curse of dimensionality in nonparametric regression (2019) — 312 citations
• A dynamic look-ahead Monte Carlo algorithm for pricing Bermudan options (2007) — 71 citations
• A Distribution-Free Theory of Nonparametric Regression (Springer Series in Statistics) (2002) — 174 citations
• Prediction from Randomly Right Censored Data (2002) — 87 citations