Fetching the paper…
Reading the bibliography…
We consider high-dimensional binary classification by sparse logistic regression.
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models
1989
Earlier work this paper cites.
Devroye, L., Györfi, L. and Lugosi, G. (1996). A Probabilistic Theory of Pattern Recognition
1996
Earlier work this paper cites.
Yang, Y. and Barron, A.R. (1998) An asymptotic property of model selection criteria. IEEE Trans. Inf. Theory
1998
Earlier work this paper cites.
Barron, A., Birgé, L. and Massart P. (1999). Risk bounds for model selection via penalization. Prob. Theory Relat. Fields
1999
Earlier work this paper cites.
Mammen, E. and Tsybakov, A. (1999). Smooth discrimination analysis. Ann. Statist
1999
Earlier work this paper cites.
Yang, Y. (1999). Minimax nonparametric classification. Parts I and II. IEEE Trans. Inf. Theory
1999
Earlier work this paper cites.
Vapnik, V.N. (2000). The Nature of Statistical Learning
2000
Earlier work this paper cites.
Birgé, L. and Massart, P. (2001). Gaussian model selection. J. Eur. Math. Soc
2001
Earlier work this paper cites.
Bickel, P. and Levina, E. (2004). Some theory for Fisher’s discriminant function, ‘naive Bayes’, and some alternatives where there are more variables than observations. Bernoulli
2004
Earlier work this paper cites.
Tsybakov, A. (2004). Optimal aggregation of classifiers in statistical learning. Ann. Statist
2004
Earlier work this paper cites.
2004
Cited alongside, same era.
Boucheron, S., Bousquet, O., and Lugosh, G. (2005). Theory of classification: a survey of some recent advances. ESAIM: Prob. Statist
2005
Cited alongside, same era.
Koltchinskii, V. and Beznosova, O. (2005). Exponential convergence rates in classification. In Learning Theory. Lecture Notes in Comput. Sci. 3559
2005
Cited alongside, same era.
Bartlett, P.L., Jordan, M.I. and McAuliffe, J.D. (2006). Convexity, classification, and risk bounds. J. Amer. Statist. Assoc
2006
Cited alongside, same era.
Massart, P. and Nédélec, E. (2006). Risk bounds for statistical learning. Ann. Statist
2006
Cited alongside, same era.
Abramovich, F. and Grinshtein, V. (2010). MAP model selection in Gaussian regression. Electr. J. Statist
2010
Later among the works it cites.
Bühlmann, P. and van de Geer, S. (2011). Statistics for High-Dimensional Data. Methods, Theory and Applications
2011
Later among the works it cites.
Rigollet, P. and Tsybakov, A. (2011). Exponential screening and optimal rates of sparse estimation. Ann. Statist
2011
Later among the works it cites.
Verzelen, N. (2012). Minimax risks for sparse regressions: Ultra-high dimensional phenomenon. Electr. J. Statist
2012
Later among the works it cites.
Bogdan, M., van den Berg, E., Sabatti, C., Su, W. and Candés, E. (2015). SLOPE – adaptive variable selection via convex programming. Ann. Appl. Statist
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Audibert, J-Y. and Tsybakov, A. (2007). Fast learning rates for plug-in classifiers. A
2007
Cited alongside, same era.
Birgé, L. and Massart, P. (2007). Minimal penalties for Gaussian model selection. Probab. Theory Relat. Fields
2007
Cited alongside, same era.
Fan, J. and Fan, Y. (2008). High-dimensional classification using feature annealed independence rules. Ann. Statist
2008
Cited alongside, same era.
van de Geer, S. (2008). High-dimensional generalized linear models and the Lasso. Ann. Statist
2008
Cited alongside, same era.
Giraud, C. (2015). Introduction to High-Dimensional Statistics
2015
Later among the works it cites.
Abramovich, F. and Grinshtein, V. (2016). Model selection and minimax estimation in generalized linear models. IEEE Trans. Inf. Theory
2016
Later among the works it cites.
Bellec, P.C., Lecué, G. and Tsybakov, A. (2018). Slope meets Lasso: improved oracle bounds and optimaility. Ann. Statist
2018
Closest in time.
Painsky, A. and Wornell, G.W. (2018). On the universality of the logistic loss function. 2018 IEEE International Symposium on Information Theory
2018
Closest in time.