Linear-time estimators for propensity scores

Journal of Machine Learning Research

Volumn 15, Issue , 2011, Pages 93-100

  Agarwal, Deepak ^a   Li, Lihong ^a   Smola, Alexander J ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL GROUPS; COVARIATE SHIFTS; LOGISTIC REGRESSIONS; MAXIMUM ENTROPY; PROPENSITY SCORE; RADON-NIKODYM DERIVATIVE; REGULARIZER; SCORING ALGORITHMS;

ALGORITHMS; LOGISTICS;

ARTIFICIAL INTELLIGENCE;

EID: 84862302374     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (23)

1. S.M. Ali and S.D. Silvey. A general class of coefficients of divergence of one distribution from another. Journal of the Royal Statistical Society. Series B (Methodological), 28(1):131-142, 1966.
2. Y. Altun and A.J. Smola. Unifying divergence minimization and statistical inference via convex duality. In H.U. Simon and G. Lugosi, editors, Proc. Annual Conf. Computational Learning Theory, LNCS, pages 139-153. Springer, 2006.
3. A. Asuncion and D.J. Newman. UCI machine learning repository, 2007.
4. P.L. Bartlett, E. Hazan, and A. Rakhlin. Adaptive online gradient descent. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, Cambridge, MA, 2008. MIT Press.
5. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, Cambridge, England, 2004.
6. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20(3):273-297, 1995.
7. A. Gretton, K. Borgwardt, M. Rasch, B. Schölkopf, and A.J. Smola. A kernel method for the two sample problem. Technical Report 157, MPI for Biological Cybernetics, 2008.
8. A. Gretton, A.J. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Schölkopf. Covariate shift by kernel mean matching. In J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, editors, Covariate Shift and Local Learning by Distribution Matching, pages 131-160, Cambridge, MA, 2008. MIT Press.
9. T. Kanamori, S. Hido, and M. Sugiyama. A least-squares approach to direct importance estimation. Journal of Machine Learning Research, 10:1391-1445, 2009.
10. D. Lambert and D. Pregibon. More bang for their bucks: assessing new features for online advertisers. SIGKDD Explorations, 9(2):100-107, 2007.
11. J. Langford, L. Li, and A.L. Strehl. Vowpal Wabbit (fast online learning), 2007. http://hunch.net/~vw/.
12. J. Langford, A.J. Smola, and M. Zinkevich. Slow learners are fast. arXiv:0911.0491, 2009.
13. D.J. Newman, S. Hettich, C.L. Blake, and C.J. Merz. UCI repository of machine learning databases, 1998.
14. J. Quiñonero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence, editors. Dataset Shift in Machine Learning. MIT Press, Cambridge, MA, 2008.
15. A. Rahimi and B. Recht. Random features for large-scale kernel machines. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, MA, 2008.
16. C.P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer, New York, 2004.
17. P. Rosenbaum and D. Rubin. The central role of propensity score in observational studies for causal effects. Biometrica, 70:41-55, 1983.
18. B. Schölkopf, J. Platt, J. Shawe-Taylor, A.J. Smola, and R.C. Williamson. Estimating the support of a high-dimensional distribution. Neural Comput., 13(7):1443-1471, 2001.
19. L. Song, J. Huang, A.J. Smola, and K. Fukumizu. Hilbert space embeddings of conditional distributions. In ICML, 2009.
20. M. Sugiyama, S. Nakajima, H. Kashima, P. von Bünau, and M. Kawanabe. Direct importance estimation with model selection and its application to covariate shift adaptation. In Advances in Neural Information Processing Systems 20, pages 1433-1440, Cambridge, MA, 2008.
21. Y. Tsuboi, H. Kashima, S. Hido, S. Bickel, and M. Sugiyama. Direct density ratio estimation for large-scale covariate shift adaptation. In SDM, pages 443-454, 2008.
22. S.V.N. Vishwanathan, N.N. Schraudolph, M. Schmidt, and K. Murphy. Accelerated training conditional random fields with stochastic gradient methods. In ICML, pages 969-976, New York, NY, USA, 2006. ACM Press.
23. M. Zinkevich. Online convex programming and generalised infinitesimal gradient ascent. In ICML, pages 928-936, 2003.