Distribution to distribution regression

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 3, 2013, Pages 2086-2094

  Oliva, Junier B ^a   Póczos, Barnabás ^a   Schneider, Jeff ^a  

^a Machine Learning Department School of Computer Science Carnegie Mellon University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

COVARIATES; DOUBLING DIMENSIONS; EFFECTIVE DIMENSIONS; INPUT DISTRIBUTIONS; OUTPUT DISTRIBUTION; UPPER BOUND;

RISK ASSESSMENT;

EID: 84897500693     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Buck, T.E., Rao, A., Coelho, L.P., Fuhrman, M.H., Jarvik, J.W., Berget, P.B., and Murphy, R.F. Cell cycle dependence of protein subcellular location inferred from static, asynchronous images. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pp. 1016-1019. IEEE, 2009.
2. Ferraty, F. and Vieu, P. Nonparametric functional data analysis: theory and practice. Springer, 2006.
3. Ingster, Y. and Stepanova, N. Estimation and detection of functions from anisotropic sobolev classes. Electronic Journal of Statistics, 5:484-506, 2011.
4. Jaakkola, T.S., Haussler, D., et al. Exploiting generative models in discriminative classifiers. Advances in neural information processing systems, pp. 487-493, 1999.
5. Jebara, T., Kondor, R., and Howard, A. Probability product kernels. The Journal of Machine Learning Research, 5:819-844, 2004.
6. Kpotufe, S. k-nn regression adapts to local intrinsic dimension. arXiv preprint arXiv:1110.4300, 2011.
7. Laurent, B. Efficient estimation of integral functionals of a density. The Annals of Statistics, 24(2):659-681, 1996.
8. Moreno, P.J., Ho, P., and Vasconcelos, N. A kullback-leibler divergence based kernel for svm classification in multimedia applications. Advances in Neural Information Processing Systems, 16:1385-1393, 2003.
9. Muandet, K., Schölkopf, B., Fukumizu, K., and Dinuzzo, F. Learning from distributions via support measure machines. arXiv preprint arXiv:1202.6504, 2012.
10. Nussbaum, M. On optimal filtering of a function of many variables in white gaussian noise. Problemy Peredachi Informatsii, 19(2):23-29, 1983.
11. Poczos, B., Rinaldo, A., Singh, A., and Wasserman, L. Distribution-Free Distribution Regression. AIS-TATS 2013, arXiv preprint arXiv:1302.0082, 2012.
12. Póczos, B., Xiong, L., and Schneider, J. Nonparametric divergence estimation with applications to machine learning on distributions. arXiv preprint arXiv:1202.3758, 2012a.
13. Póczos, B., Xiong, L., Sutherland, D.J., and Schneider, J. Nonparametric kernel estimators for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2989-2996. IEEE, 2012b.
14. Ramsay, J.O. and Silverman, B.W. Applied functional data analysis: methods and case studies, volume 77. Springer New York:, 2002.
15. Rigollet, P. and Vert, R. Optimal rates for plug-in estimators of density level sets. Bernoulli, 15(4):1154-1178, 2009.
16. Smola, A., Gretton, A., Song, L., and Schölkopf, B. A hilbert space embedding for distributions. In Algorithmic Learning Theory, pp. 13-31. Springer, 2007.
17. Tsybakov, Alexandre B. Introduction to nonparametric estimation. Springer, 2008.