A. Thesis and Books

1. M.Sc. thesis: On the farthest point problem.
   
   

2. Ph.D. thesis: Mathematical aspects of learning in Neural Networks.
   
   

3. Advanced Lectures in Machine Learning, (S. Mendelson, A.J. Smola Eds), LNCS 2600, Springer 2003. 

1. S. Mendelson, On the size of convex hulls of small sets, 
   Journal of Machine Learning Research 2, 1-18, 2001.
   
   

2. S. Mendelson, l-norm and its application to Learning Theory, 
   Positivity, 5, 177-191, 2001.
   
   

3. S. Mendelson, A new on-line learning model, 
   Neural Computation 13(4), 935-957, April 2001.
   
   

4. S. Mendelson and I. Nelken, Recurrence techniques in the analysis of neural networks, 
   Neural Computation 13(8) 1839-1861, August 2001.
   
   

5. S. Mendelson, Rademacher averages and phase transitions in Glivenko-Cantelli classes, 
   IEEE Transactions on Information Theory, 48(1), 251-263, 2002.
   
   

6. S. Mendelson, Improving the sample complexity using global data,
   IEEE Transactions on Information Theory 48(7), 1977-1991, 2002.
   
   

7. S. Mendelson, Learnability in Hilbert spaces with Reproducing Kernels, 
   Journal of Complexity, 18(1), 152-170, 2002.
   
   

8. P.L. Bartlett, S. Mendelson, Rademacher and Gaussian complexities: risk bounds and structural results (extended version of conference paper (5)), 
   Journal of Machine Learning Research 3, 463-482, 2002
   
   

9. S. Mendelson, R. Vershynin, Entropy and the combinatorial dimension,
   Inventiones Mathematicae, 152(1), 37-55, 2003.
   
   

10. S. Mendelson, A few notes on Statistical Learning Theory,
    In  Advanced Lectures in Machine Learning, (S. Mendelson, A.J. Smola Eds),
    LNCS 2600, 1-40, Springer 2003.
    
    

11. S. Mendelson, Estimating the performance of kernel classes,
    Journal of Machine Learning Research, 4, 759-771, 2003.
    
    

12. G. Lugosi, S. Mendelson, V. Koltchinskii, A note on the richness of convex hulls of VC classes,
    Electronic communications in Probability, 8, 167-169, 2003.
    
    

13. S. Mendelson, G. Schechtman, The shattering dimension of sets of linear functionals,
    Annals of Probability, 32(3A), 1746-1770, 2004.
    
    

14. S. Mendelson, Geometric parameters in Learning Theory.
    GAFA lecture notes, LNM 1850, 193-236, 2004.
    
    

15. S. Mendelson, P. Philips, On the importance of "small" coordinate projections.
    Journal of Machine Learning Research, 5(Mar), 219-238, 2004.
    
    

16. S. Mendelson, R. Vershynin, Remarks on the geometry of coordinate projections in R^n.
    Israel Journal of Mathematics, 140, 203-220, 2004.
    
    

17. F. Barthe, O. Guedon, S. Mendelson, A. Naor, A probabilistic approach to the geometry of the $\ell_p^n$ ball,
    Annals of Probability, 33(2), 480-513, 2005. 
    
    

18. S. Mendelson, Embeddings with a Lipschitz function,
    Random Structures and Algorithms, 27(1) 25-45, 2005.
    
    

19. S. Mendelson, A. Pajor, N. Tomczak-Jaegermann, Reconstruction and subgaussian processes,
    CRAS 340(12) 885-888, 2005.
    
    

20. P.L. Bartlett, O. Bousquet, S. Mendelson, Local Rademacher Complexities,
    Annals of Statistic, 33(4) 1497-1537, 2005.
    
    

21. B. Klartag, S. Mendelson, Empirical Processes and Random Projections,
    Journal of Functional Analysis, 225(1) 229-245, 2005.
    
    

22. S. Mendelson, A. Pajor, M. Rudelson, On the Geometry of random {-1,1}-polytopes,
    Discrete and Computational Geometry, 33(3) 365-379, 2005.
    
    

23. P.L. Bartlett, S. Mendelson, Empirical minimization,
    Probability Theory and Related Fields, 135, 311-334, 2006.
     

24. S. Mendelson, Lipschitz representations of subsets of the cube,
    Proceedings of the AMS, 135, 1455-1463, 2007.
     

25. S. Mendelson, A. Pajor, On singular values of matrices with independent rows,
    Bernoulli, 12(5), 761-773, 2006.
     

26. P.L. Bartlett, S. Mendelson, Local Rademacher complexities and empirical minimization,
    Annals of Statistics, vol 34, 2006 (9 pages).
     

27. S. Mendelson, J. Zinn, Modified Empirical CLT's under only pre-Gaussian conditions,
    High Dimensional Probability, in IMS lecture notes monograph series, vol 51, 173-184, 2006.
     

28. N. Linial, S. Mendelson, G. Schechtman, A. Schraibman, Complexity measures of sign matrices,
    Combinatorica, to appear (24 pages).
     

29. S. Mendelson, A. Pajor, N. Tomczak-Jaegermann, Reconstruction and subgaussian operators in Asymptotic Geometric Analysis,  
    Geometric and Functional Analysis, 17(4), 1248-1282, 2007.

     

30. S. Mendelson, N. Tomczak-Jaegermann, A subgaussian embedding theorem,
    Israel Journal of Mathematics, to appear  (15 pages).
     

31. O. Guedon, S. Mendelson, A. Pajor, N. Tomczak-Jaegermann, Subspaces and orthogonal decompositions generated by bounded orthogonal systems,
    Positivity, 11(2), 269-283, 2007.
     

32. S. Mendelson, On weakly bounded empirical processes,
    Math. Annalen, 340(2), 293-314, 2008.
     

33. S. Mendelson, A. Pajor, N. Tomczak-Jaegermann, Uniform uncertainty principle for Bernoulli and subgaussian ensembles,
    Constructive Approximation, to appear (15 pages).
     

34. Y. Gordon, A. Litvak, S. Mendelson, A. Pajor, Gaussian averages of interpolated bodies,
    Journal of Approximation Theory, in press, DOI 10.1016/j.jat2007.04.007, (17 pages).
     

35. S. Mendelson, Obtaining fast error rates in nonconvex situations,
    Journal of Complexity, to appear, (24 pages).
     

36. P.L. Bartlett, S. Mendelson, P. Philips, Optimal sample based estimates of the expectation of the empirical minimizer, ESAIM Probability and Statistics, to appear (29 pages).
     

37. S. Mendelson, Lower bounds for the empirical minimization algorithm, IEEE Transactions on Information Theory, to appear (19 pages).
    
    
    
    

1. S. Mendelson, N. Tishby, Statistical Sufficiency for classes in empirical L₂ spaces,
   Proceedings of the 13th annual conference on Computational Learning Theory COLT00, 81-89, 2000.
   
   

2. S. Mendelson, Geometric Methods in the Analysis of Glivenko-Cantelli Classes,  
   Proceedings of the 14^th annual conference on Computational Learning Theory COLT01, 256-272, 2001.
   
   

3. S. Mendelson, Learning Relatively Small Classes, 
   Proceedings of the 14^th annual conference on Computational Learning Theory COLT01, 273-288, 2001.
   
   

4. P. L. Bartlett, S. Mendelson, Rademacher and gaussian complexities: risk bounds and structural results, Proceedings of the 14^th annual conference on Computational Learning Theory COLT01, 224-240, 2001.
   
   

5. S. Mendelson, R.C. Williamson, Agnostic learning of non-convex classes of functions, Proceedings of the 15th annual conference on Computational Learning Theory COLT02, 1-13, 2002.
   
   

6. S. Mendelson, R. Vershynin, Entropy, combinatorial dimensions and random averages,  Proceedings of the 15th annual conference on Computational Learning Theory COLT02, 14-28, 2002.
   
   

7. S. Mendelson, Geometric parameters of kernel machines, 
   Proceedings of the 15th annual conference on Computational Learning Theory COLT02, 29-43, 2002.
   
   

8. P.L. Bartlett, O. Bousquet, S. Mendelson, Localized Rademacher Averages,
   Proceedings of the 15th annual conference on Computational Learning Theory COLT02, 44-58, 2002.
   
   

9. S. Mendelson, P. Philips, Random subclass bounds, Proceedings of the 16th annual conference on Learning Theory COLT03, Bernhard Schoelkopf and Manfred Warmuth (Eds.), Lecture Notes in Computer Sciences 2777, Springer, 329-343, 2003.
   
   

10. P.L. Bartlett, S. Mendelson, P. Philips, Local complexities for empirical risk minimization, Proceedings of the 17th annual conference on Learning Theory COLT04, John Shawe-Taylor, Yoram Singer (Eds.), Lecture Notes in Computer Sciences 3120, Springer, 270-284, 2004.
    
    

11. S. Mendelson, A. Pajor, Ellipsoid approximation with random vectors,
    Proceedings of the 18th annual conference on Learning Theory COLT05, Peter Auer, Ron Meir (Eds.), Lecture Notes in Computer Sciences 3559, Springer, 429-433, 2005.
    
    

12. S. Mendelson, On the limitations of embedding methods,
    Proceedings of the 18th annual conference on Learning Theory COLT05, Peter Auer, Ron Meir (Eds.), Lecture Notes in Computer Sciences 3559, Springer, 353-365, 2005.

    
    

D. Original Journal Papers (submitted)

1. S. Mendelson, Discrepancy, chaining and Subgaussian processes (39 pages).

2. O. Guedon, S. Mendelson, A. Pajor, N. Tomczak-Jaegermann, Majorizing measures and proportional subsets of
   bounded orthonormal systems, (18 pages).

3. G. Lecue, S. Mendelson, Aggregation vs. Empirical risk minimization (21 pages).

4. S. Mendelson, J. Neeman, Regularization in Kernel Learning (43 pages).

1. S. Mendelson, On the Geometry of Glivenko-Cantelli Classes.
   
   
2. S. Mendelson and G. Wolansky, Stochastic Gradient Method for Parabolic-Type Systems and Application to Variational Neural Mappings.
   
   
3. S. Mendelson, Geometric Methods in the Analysis of Kernel Machines.
   
   
4. S. Axelrod, S. Fine, R. Gilad-Bachrach, S. Mendelson, N. Tishby, The Information given by Observations and its Application to Active Learning with Uncertainty.