Fetching the paper…
Reading the bibliography…
``Localization'' has proven to be a valuable tool in the Statistical Learning literature as it allows sharp risk bounds in terms of the problem geometry.
Dupacovà, J. and Wets, R.J-B.: Asymptotic behavior of statistical estimators and of optimal solutions of stochastic optimization problems, Ann. Statist. 16(4), 1517–1549 (1988)
1988
Earlier work this paper cites.
Shapiro, A.: Asymptotic properties of statistical estimators in stochastic programming, Ann. Statist. 17, 841-858 (1989)
1989
Earlier work this paper cites.
King, A.J. and Wets, R.J-B.: Epi-consistency of convex stochastic programs, Stoch. Stoch. Rep. 34, 83-92 (1991)
1991
Earlier work this paper cites.
Shapiro, A.: Asymptotic analysis of stochastic programs, Ann. Oper. Res. 30, 169-186 (1991)
1991
Earlier work this paper cites.
King, A.J. and Rockafellar, R.T.: Asymptotic theory for solutions in statistical estimation and stochastic programming, Math. Oper. Res. 18, 148-162 (1993)
1993
Earlier work this paper cites.
Talagrand, M.: Sharper bounds for Gaussian and empirical processes, Annals of Probability 22, 28-76 (1994)
1994
Earlier work this paper cites.
Artstein, Z. and Wets, R.J-B.: Consistency of minimizers and the SLLN for stochastic programs, Journal of Convex Analysis 2, 1-17 (1995)
1995
Earlier work this paper cites.
Pflug, G.C.: Asymptotic stochastic programs, Math. Oper. Res. 20, 769-789 (1995)
1995
Earlier work this paper cites.
Tibshirani, R.: Regression shrinkage and selection via the Lasso , J. Roy. Statist. Soc. Ser. B 58, 267–288 (1996)
1996
Earlier work this paper cites.
Pang, J-S.: Error bounds in mathematical programming , Mathematical Programming Ser. B 79(1), 299–332 (1997)
1997
Earlier work this paper cites.
Pflug, G.C.: Stochastic programs and statistical data, Annals of Operations Research 85, 59-78 (1999)
1999
Earlier work this paper cites.
Rockafellar, R.T. and Urysaev, S.: Optimization of conditional value-at-risk, Journal of Risk 2(3), 493-517 (2000)
2000
Earlier work this paper cites.
Panchenko, D.: Symmetrization approach to concentration inequalities for empirical processes , The Annals of Probability 31, 2068–2081 (2003)
2003
Earlier work this paper cites.
Pflug, G.C.: Stochastic optimization and statistical inference. In: Ruszczyński, A. and Shapiro, A. (eds.) Handbooks in OR & MS, Vol. 10, pp. 427-482. Elsevier (2003)
2003
Earlier work this paper cites.
Römisch, W.: Stability of Stochastic Programming Problems. In: Ruszczyński, A. and Shapiro, A. (eds.) Handbooks in OR & MS, Vol. 10, pp. 483-554. Elsevier (2003)
2003
Earlier work this paper cites.
Shapiro, A.: Monte Carlo sampling methods. In: Ruszczyński, A. and Shapiro, A. (eds.) Handbooks in OR & MS, Vol. 10, pp. 353-425. Elsevier (2003)
2003
Earlier work this paper cites.
Bunea, F., Tsybakov, A.B. and Wegkamp, M. H.: Aggregation for regression learning. Preprint: https://arxiv.org/abs/math/0410214 (2004)
2004
Earlier work this paper cites.
Greenshtein, E., Ritov, Y.: Persistence in high-dimensional linear predictor selection and the virtue of overparametrization. Bernoulli 10(6), 971–988 (2004)
2004
Earlier work this paper cites.
Bartlett, P., Bousquet, O. and Mendelson, S.: Local Rademacher complexities. Ann. Statist. 33 1497–1537 (2005)
2005
Earlier work this paper cites.
Bartlett, P. and Mendelson, S.: Empirical minimization. Probability Theory and Related Fields 135 (3), 311–334 (2006)
2006
Cited alongside, same era.
Bunea, F., Tsybakov, A.B. and Wegkamp, M. H.: Aggregation and sparsity via ℓ 1 \ell_{1} -penalized least squares. In: Lugosi G., Simon H.U. (eds) Learning Theory. COLT 2006. Lecture Notes in Computer Science, vol 4005. Springer, Berlin, Heidelberg
2006
Cited alongside, same era.
Greenshtein, E.: Best subset selection, persistence in high-dimensional statistical learning and optimization under ℓ 1 \ell_{1} constraint. Ann. Statist. 34(5), 2367–2386 (2006)
2006
Cited alongside, same era.
Koltchinskii, V.: Local Rademacher complexities and oracle inequalities in risk minimization. Ann. Statist. 34 (6), 2593-2656 (2006)
2006
Cited alongside, same era.
Leng, C., Lin, Y., Wahba, G.: A note on the lasso and related procedures in model selection. Statistica Sinica 16, 1273–1284 (2006)
Koltchinskii, V.: Sparse recovery in convex hulls via entropy penalization. Ann. Statist. 37(3), 1332–1359 (2009)
2009
Later among the works it cites.
Meinshausen, N. and Yu, B.: Lasso-type recovery of sparse representations for high-dimensional data. Ann. Statist. 37(1), 246–270 (2009)
2009
Later among the works it cites.
Shapiro, A.. Dentcheva, D. and Ruszczynski, A.: Lectures on Stochastic Programming: Modeling and Theory. MOS-SIAM Ser. Optim., SIAM, Philadelphia, (2009)
2009
Later among the works it cites.
Zhang, T.: Some sharp performance bounds for least squares regression with L1 regularization. Ann. Statist. 37(5A), 2109–2144 (2009)
2009
Later among the works it cites.
Koltchinskii, V.: Oracle inequalities in empirical risk minimization and sparse recovery problems. Lecture Notes in Mathematics book series (LNM, volume 2033), Ecole d’Eté Probabilit. Saint-Flour book sub series (LNMECOLE, volume 2033), Springer-Verlag Berlin Heidelberg, (2011)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2006
Cited alongside, same era.
Meinhausen, N and Bühlmann, P.: High-dimensional graphs and variable selection with the Lasso. Ann. Statist. 34(3), 1436–1462 (2006)
2006
Cited alongside, same era.
Zhao, P. and Yu, B.: On model selection consistency of Lasso. Journal of Machine Learning Research 7, 2541–2563 (2006)
2006
Cited alongside, same era.
2006
Cited alongside, same era.
Bunea, F., Tsybakov, A.B. and Wegkamp, M. H.: Sparsity oracle inequalities for the Lasso. Electron. J. Statist. 1, 169–194 (2007)
2007
Cited alongside, same era.
Bunea, F., Tsybakov, A.B. and Wegkamp, M. H.: Aggregation for Gaussian regression. Ann. Statist. 35(4), 1674–1697 (2007)
2007
Cited alongside, same era.
Bunea, F., Tsybakov, A.B. and Wegkamp, M. H.: Sparse density estimation with ℓ 1 \ell_{1} penalties. In: Bshouty N.H., Gentile C. (eds) Learning Theory. COLT 2007. Lecture Notes in Computer Science, vol 4539. Springer, Berlin, Heidelberg
2007
Cited alongside, same era.
Candes, E. and Tao, T.: The Dantzig selector: Statistical estimation when p is much larger than n. Ann. Statist. 35(6), 2313–2351 (2007)
2007
Cited alongside, same era.
2011
Later among the works it cites.
Koltchinskii, V., Lounici, K. and Tsybakov, A.B.: Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion. Ann. Statist. 39(5), 2302–2329 (2011)
2011
Later among the works it cites.
Barlett, P.L., Mendelson, S. and Neeman, J.: ℓ 1 \ell_{1} -regularized linear regression: persistence and oracle inequalities, Probab. Theory Relat. Fields 154, 193–224 (2012)
2012
Later among the works it cites.
Lecué, G.and Mendelson, S.: General nonexact oracle inequalities for classes with subexponential envelope. Ann. Statist. 40(2), 832–860 (2012)
2012
Later among the works it cites.
2013
Later among the works it cites.
Homem-de-Mello, T. and Bayraksan, G.: Monte Carlo sampling-based methods for stochastic optimization, Surveys in Operations Research and Management Science, 19, 56-85 (2014)
2014
Later among the works it cites.
Talagrand, M.: Upper and lower bounds for stochastic processes
2014
Later among the works it cites.
Kim, S., Pasupathy, R. and Henderson, S.G.: A guide to Sample Average Approximation. In: Michael Fu (ed.), Handbook of Simulation Optimization, International Series in Operations Research & Management Science, Vol. 216, pp. 207-243. Springer, New York (2015)
2015
Later among the works it cites.
Oliveira, R.I.: The lower tail of random quadratic forms with applications to ordinary least squares , Probab. Theory Relat. Fields 166, 1175–1194 (2016)
2016
Later among the works it cites.
Guigues, V., Juditsky, A. and Nemirovski, A.: Non-asymptotic confidence bounds for the optimal value of a stochastic program, Optimization Methods and Software 32(5), 1033–1058 (2017)
2017
Closest in time.
Lecué, G. and Mendelson, S.: Sparse recovery under weak moment assumptions. J. Eur. Math. Soc. 19, 881–904 (2017)
2017
Closest in time.
Bellec, P.C., Lecué, G. and Tsybakov, A.B.: Slope meets lasso: improved oracle bounds and optimality. Ann. Statist. 46(6B), 3603–3642 (2018)
2018
Closest in time.
AN Iusem, A Jofré, P Thompson, Incremental constraint projection methods for monotone stochastic variational inequalities, Mathematics of Operations Research 44 (1), 236-263 (2019)
2019
Closest in time.
Oliveira, R.I. and Thompson, P.: Sample average approximation with heavier tails I: non-asymptotic bounds with weak assumptions and stochastic constraints , Mathematical Programming (2022), https://doi.org/10.1007/s10107-022-01810-x
2022
Closest in time.