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A Distribution-Free Theory of Nonparametric Regression
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Citations
Cited by
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149
A survey of cross-validation procedures for model selection
Arlot, S., & Celisse, A. (2010) · Statistics Surveys · doi:10.1214/09-ss054 · Cited by 3,522
Algorithms for Reinforcement Learning
Szepesvári, C. (2010) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01551-9 · Cited by 1,049
Covariance regularization by thresholding
Bickel, P. J., & Levina, E. (2008) · The Annals of Statistics · doi:10.1214/08-aos600 · Cited by 893
Scornet, E., Biau, G., & Vert, J.-P. (2015) · The Annals of Statistics · doi:10.1214/15-aos1321 · Cited by 612
Distinguishing cause from effect using observational data: methods and benchmarks
Mooij, J. M., Peters, J., Janzing, D., Jakob Zscheischler, A., & Schölkopf, B. (2014) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1412.3773 · Cited by 302
Random Forests and Kernel Methods
Scornet, E. (2016) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2514489 · Cited by 296
Breaking the Curse of Dimensionality with Convex Neural Networks
Bach, F. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.8690 · Cited by 295
Nonparametric regression using deep neural networks with ReLU activation function
Schmidt-Hieber, J. (2020) · The Annals of Statistics · doi:10.1214/19-aos1875 · Cited by 292
Cattaneo, M. D., Crump, R. K., Farrell, M. H., & Feng, Y. (2024) · American Economic Review · doi:10.1257/aer.20221576 · Cited by 139
Kruppa, J., Liu, Y., Biau, G., Kohler, M., König, I. R., Malley, J. D., & Ziegler, A. (2014) · Biometrical Journal · doi:10.1002/bimj.201300068 · Cited by 97
Mix-n-match: Ensemble and compositional methods for uncertainty calibration in deep learning
Zhang, J., Kailkhura, B., & Han, Y.-J. (2020) · Unpublished · doi:10.13140/rg.2.2.11390.13125 · Cited by 86
Post-Estimation Smoothing: A Simple Baseline for Learning with Side Information
Rolf, E., Jordan, M. I., & Recht, B. (2020) · arXiv · doi:10.48550/arxiv.2003.05955 · Cited by 3
Metadata
- Authors
- Györfi, L., Kohler, M., Krzyżak, A., & Walk, H.
- Venue
- Springer Series in Statistics
- Publisher
- Springer New York
- Published
- 2002
- ISSN
- 0172-7397 (ISSN-L)
- OpenAlex
- W1484867920
- Cited by
- 1,738
- References
- 0
- FWCI
- 5.90
- Type
- book
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.