Distribution-free distribution regression

Journal of Machine Learning Research

Volumn 31, Issue , 2013, Pages 507-515

  Póczos, Barnabás ^a   Rinaldo, Alessandro ^b   Singh, Aarti ^a   Wasserman, Larry ^a,b  

^a Machine Learning Department ^*  (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; REGRESSION ANALYSIS; RISK ASSESSMENT; RISK PERCEPTION;

COVARIATES; DISTRIBUTION-FREE; DOUBLING DIMENSIONS; EFFECTIVE DIMENSIONS; ERROR TERMS; PREDICTION RISKS; REGRESSION FUNCTION; THEORY AND METHODS;

PROBABILITY DISTRIBUTIONS;

EID: 84923315975     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (19)

1. R.J. Carroll, D. Ruppert, L.A. Stefanski, and C.M. Crainiceanu. Measurement error in nonlinear mod els: A modern perspective, volume 105. Chapman & Hall/CRC, 2006.
2. A. Christmann and I. Steinwart. Universal kernels on non-standard input spaces. In NIPS, pages 406-414, 2010.
3. L. Devroye and G. Lugosi. Combinatorial methods in density estimation. Springer, 2001.
4. J. Fan and Y.K. Truong. Nonparametric regression with errors in variables. The Annals of Statistics, pages 1900-1925, 1993.
5. F. Ferraty and P. Vieu. Nonparametric Functional Data Analysis: Theory and Practice. Springer Verlag, 2006.
6. L. Györ, M. Kohler, A. Krzyzak, and H. Walk. A Distribution-Free Theory of Nonparametric Regression. Springer, New-york, 2002.
7. T. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In NIPS, pages 487-493. MIT Press, 1998.
8. T. Jebara, R. Kondor, A. Howard, K. Bennett, and N. Cesa-bianchi. Probability product kernels. JMLR, 5: 819-844, 2004.
9. R. Kondor and T. Jebara. A kernel between sets of vectors. In ICML, 2003.
10. S. Kpotufe. k-nn regression adapts to local intrinsic dimension. arXiv preprint arXiv: 1110.4300, 2011.
11. P. Moreno, P. Ho, and N. Vasconcelos. A Kullback- Leibler divergence based kernel for SVM classification in multimedia applications. In NIPS, 2004.
12. K. Muandet, B. Schölkopf, K. Fukumizu, and F. Dinuzzo. Learning from distributions via support measure machines. arXiv.org, stat.ML, February 2012.
13. B. Poczos, L. Xiong, and J. Schneider. Nonparametric divergence estimation with applications to machine learning on distributions. In UAI, 2011.
14. B. Poczos, L. Xiong, D. Sutherland, and J. Schneider. Nonparametric kernel estimators for image classifi- cation. In Computer Vision and Pattern Recognition, 2012.
15. B. Poczos, A. Rinaldo, A. Singh, and L. Wasserman. Distribution-free distribution regression, 2013. http://arxiv.org/abs/1302.0082.
16. J.O. Ramsay and B.W Silverman. Functional data analysis. Springer, New York, 2nd edition, 2005.
17. P. Rigollet and R. Vert. Optimal rates for plug-in estimators of density level sets. Bernoulli, 15(4): 1154-1178, 2009.
18. A. Smola, A. Gretton, L. Song, and B. Schölkopf. A Hilbert space embedding for distributions. In ALT, 2007.
19. A.B. Tsybakov. Introduction to Nonparametric Estimation. Springer, 2010.