Fetching the paper…
Reading the bibliography…
We consider the problem of regression learning for deterministic design and independent random errors.
Haussler, D., Kivinen, J., Warmuth, M., 1998. Sequential prediction of individual sequences under general loss functions. IEEE Trans. Inform. Theory 44 (5), 1906–1925
1925
Earlier work this paper cites.
Kent, J., 1978. Time-reversible diffusions. Adv. in Appl. Probab. 10 (4), 819–835
1978
Earlier work this paper cites.
Rogers, L., Williams, D., 1987. Diffusions, Markov processes, and martingales. Vol. 2. Probability and Mathematical Statistics. John Wiley & Sons Inc., New York
1987
Earlier work this paper cites.
Vovk, V. G., 1989. Prediction of stochastic sequences. Problemy Peredachi Informatsii 25 (4), 35–49
1989
Earlier work this paper cites.
Vovk, V., 1990. Aggregating strategies. In: COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann Publishers. pp. 371–386
1990
Earlier work this paper cites.
Meyn, S. P., Tweedie, R. L., 1993. Markov chains and stochastic stability. Communications and Control Engineering Series. Springer-Verlag London Ltd., London
1993
Earlier work this paper cites.
Littlestone, N., Warmuth, M. K., 1994. The weighted majority algorithm. Inform. and Comput. 108 (2), 212–261
1994
Earlier work this paper cites.
Cesa-Bianchi, N., Freund, Y., Haussler, D., Helmbold, D. P., Schapire, R. E., Warmuth, M. K., 1997. How to use expert advice. J. ACM 44 (3), 427–485
1997
Earlier work this paper cites.
Kivinen, J., Warmuth, M. K., 1999. Averaging expert predictions. In: Computational learning theory (Nordkirchen, 1999). Vol. 1572 of Lecture Notes in Comput. Sci. Springer, Berlin, pp. 153–167
1999
Earlier work this paper cites.
Roberts, G. O., Stramer, O., 2002b. Langevin diffusions and Metropolis-Hastings algorithms. Methodol. Comput. Appl. Probab. 4 (4), 337–357 (2003), international Workshop in Applied Probability (Caracas, 2002)
2002
Earlier work this paper cites.
McAllester, D., 2003. Pac-Bayesian stochastic model selection. Machine Learning 51 (1), 5–21
2003
Earlier work this paper cites.
Catoni, O., 2004. Statistical learning theory and stochastic optimization. Lecture Notes in Mathematics. Springer-Verlag, Berlin
2004
Earlier work this paper cites.
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., 2004. Least angle regression. Ann. Statist. 32 (2), 407–499
2004
Earlier work this paper cites.
Yang, Y., 2004. Aggregating regression procedures to improve performance. Bernoulli 10 (1), 25–47
2004
Earlier work this paper cites.
Johnstone, I., Silverman, B., 2005. Empirical Bayes selection of wavelet thresholds. Ann. Statist 33, 1700–1752
2005
Earlier work this paper cites.
2005
Cited alongside, same era.
Bunea, F., Tsybakov, A. B., Wegkamp, M., 2006. Aggregation and sparsity via l 1 l_{1} penalized least squares. In: Learning theory. Vol. 4005 of Lecture Notes in Comput. Sci. Springer, Berlin, pp. 379–391
2006
Cited alongside, same era.
Candès, E., Tao, T., 2006. Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inform. Theory 52 (12), 5406–5425
2006
Cited alongside, same era.
Cesa-Bianchi, N., Lugosi, G., 2006. Prediction, learning, and games. Cambridge University Press, Cambridge
2006
Cited alongside, same era.
Donoho, D., Elad, M., Temlyakov, V., 2006. Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Trans. Inform. Theory 52 (1), 6–18
Yu, B., 2007. Embracing statistical challenges in the information technology age. Technometrics 49 (3), 237–248
2007
Later among the works it cites.
Alquier, P., 2008. Pac-Bayesian bounds for randomized empirical risk minimizers. Math. Methods Statist. 17 (4), 1–26
2008
Later among the works it cites.
Dalalyan, A. S., Tsybakov, A. B., 2008. Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity. Mach. Learn. 72 (1-2), 39–61
2008
Later among the works it cites.
Juditsky, A., Rigollet, P., Tsybakov, A., 2008. Learning by mirror averaging. Ann. Statist. 36, 2183–2206
2008
Later among the works it cites.
Seeger, M. W., 2008. Bayesian inference and optimal design for the sparse linear model. J. Mach. Learn. Res. 9, 759–813
2008
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2006
Cited alongside, same era.
Leung, G., Barron, A., 2006. Information theory and mixing least-squares regressions. IEEE Trans. Inform. Theory 52 (8), 3396–3410
2006
Cited alongside, same era.
Meinshausen, N., Bühlmann, P., 2006. High-dimensional graphs and variable selection with the Lasso. Ann. Statist. 34 (3), 1436–1462
2006
Cited alongside, same era.
Rivoirard, V., 2006. Non linear estimation over weak Besov spaces and minimax Bayes method. Bernoulli 12 (4), 609–632
2006
Cited alongside, same era.
Zhao, P., Yu, B., 2006. On model selection consistency of Lasso. J. Mach. Learn. Res. 7, 2541–2563
2006
Cited alongside, same era.
Abramovich, F., Grinshtein, V., Pensky, M., 2007. On optimality of Bayesian testimation in the normal means problem. Ann. Statist. 35, 2261–2286
2007
Cited alongside, same era.
Candès, E., Tao, T., 2007. The Dantzig selector: statistical estimation when p p is much larger than n n . Ann. Statist. 35 (6), 2313–2351
2007
Cited alongside, same era.
Catoni, O., 2007. Pac-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning. Vol. 56. IMS Lecture Notes Monograph Series
2007
Cited alongside, same era.
Tsybakov, A. B., 2008. Introduction to Nonparametric Estimation. Springer Publishing Company, Incorporated
2008
Later among the works it cites.
van de Geer, S., 2008. High-dimensional generalized linear models and the Lasso. Ann. Statist. 36 (2), 614–645
2008
Later among the works it cites.
Zhang, C., Huang, J., 2008. The sparsity and biais of the Lasso selection in high-dimensional linear regression. Ann. Statist. 36, 1567–1594
2008
Later among the works it cites.
Audibert, J.-Y., 2009. Fast learning rates in statistical inference through aggregation. Ann. Statist. 37 (4), 1591–1646
2009
Closest in time.
Bickel, P. J., Ritov, Y., Tsybakov, A. B., 2009. Simultaneous analysis of lasso and Dantzig selector. Ann. Statist. 37 (4), 1705–1732
2009
Closest in time.
Dalalyan, A. S., Tsybakov, A. B., 2009. Sparse regression learning by aggregation and Langevin Monte-Carlo. In: COLT-2009
2009
Closest in time.
2009
Closest in time.
Koltchinskii, V., 2009. Sparse recovery in convex hulls via entropy penalization. Ann. Statist. 37 (3), 1332–1359
2009
Closest in time.
Cesa-Bianchi, N., Conconi, A., Gentile, C., 2004. On the generalization ability of on-line learning algorithms. IEEE Trans. Inform. Theory 50 (9), 2050–2057
2057
Closest in time.