Year |
Citation |
Score |
2013 |
Koltchinskii V. Sharp oracle inequalities in low rank estimation Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. 217-230. DOI: 10.1007/978-3-642-41136-6_19 |
0.349 |
|
2011 |
Koltchinskii V. Linear programming in sparse recovery Lecture Notes in Mathematics. 2033: 121-149. DOI: 10.1007/978-3-642-22147-7_7 |
0.373 |
|
2011 |
Koltchinskii V. Bounding expected sup-norms of empirical and Rademacher processes Lecture Notes in Mathematics. 2033: 33-57. DOI: 10.1007/978-3-642-22147-7_3 |
0.379 |
|
2010 |
Koltchinskii V, Yuan M. Sparsity in multiple kernel learning Annals of Statistics. 38: 3660-3695. DOI: 10.1214/10-Aos825 |
0.302 |
|
2010 |
Koltchinskii V. Rademacher complexities and bounding the excess risk in active learning Journal of Machine Learning Research. 11: 2457-2485. |
0.303 |
|
2006 |
Martínez-Ramón M, Koltchinskii V, Heileman GL, Posse S. fMRI pattern classification using neuroanatomically constrained boosting. Neuroimage. 31: 1129-41. PMID 16529955 DOI: 10.1016/J.Neuroimage.2006.01.022 |
0.353 |
|
2005 |
Koltchinskii V, Panchenko D. Complexities of convex combinations and bounding the generalization error in classification Annals of Statistics. 33: 1455-1496. DOI: 10.1214/009053605000000228 |
0.684 |
|
2005 |
Lozano F, Koltchinskii V. Self bounding genetic algorithms for machine learning Proceedings - Icmla 2005: Fourth International Conference On Machine Learning and Applications. 2005: 343-350. DOI: 10.1109/ICMLA.2005.57 |
0.314 |
|
2004 |
Koltchinskii V, Yu B. Three papers on boosting: An introduction Annals of Statistics. 32: 12. DOI: 10.1214/Aos/1079120127 |
0.343 |
|
2004 |
Giné E, Koltchinskii V, Sakhanenko L. Kernel density estimators: Convergence in distribution for weighted sup-norms Probability Theory and Related Fields. 130: 167-198. DOI: 10.1007/S00440-004-0339-X |
0.328 |
|
2003 |
Koltchinskii V. Bounds on margin distributions in learning problems Annales De L'Institut Henri Poincare (B) Probability and Statistics. 39: 943-978. DOI: 10.1016/S0246-0203(03)00023-2 |
0.316 |
|
2003 |
Koltchinskii V. Entropy bounds for restricted convex hulls Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2777: 741-742. |
0.314 |
|
2003 |
Koltchinskii V, Panchenko D, Lozano F. Bounding the generalization error of convex combinations of classifiers: Balancing the dimensionality and the margins Annals of Applied Probability. 13: 213-252. |
0.644 |
|
2003 |
Koltchinskii V, Panchenko D, Andonova S. Generalization bounds for voting classifiers based on sparsity and clustering Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2777: 492-505. |
0.615 |
|
2002 |
Abdallah CT, Amato F, Ariola M, Dorato P, Koltchinskii V. Statistical learning methods in linear algebra and control problems: The example of finite-time control of uncertain linear systems Linear Algebra and Its Applications. 351: 11-26. DOI: 10.1016/S0024-3795(01)00599-7 |
0.374 |
|
2002 |
Bousquet O, Koltchinskii V, Panchenko D. Some local measures of complexity of convex hulls and generalization bounds Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2375: 59-73. |
0.655 |
|
2002 |
Koltchinskii V, Panchenko D. Empirical margin distributions and bounding the generalization error of combined classifiers Annals of Statistics. 30: 1-50. |
0.674 |
|
2001 |
Ariola M, Abdallah CT, Koltchinskii V. Applications of Statistical-Learning Methods in Systems and Control Ifac Proceedings Volumes. 34: 175-180. DOI: 10.1016/S1474-6670(17)41618-1 |
0.332 |
|
2001 |
Koltchinskii V, Abdallah CT, Ariola M, Dorato P. Statistical learning control of uncertain systems: Theory and algorithms Applied Mathematics and Computation. 120: 31-43. DOI: 10.1016/S0096-3003(99)00283-0 |
0.334 |
|
2001 |
Koltchinskii V, Panchenko D, Lozano F. Further explanation of the effectiveness of voting methods: The game between margins and weights Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2111: 241-255. |
0.665 |
|
2001 |
Koltchinskii V, Panchenko D, Lozano F. Some new bounds on the generalization error of combined classifiers Advances in Neural Information Processing Systems. |
0.693 |
|
2000 |
Koltchinskii V, Abdallah CT, Ariola M, Dorato P, Panchenko D. Improved sample complexity estimates for statistical learning control of uncertain systems Ieee Transactions On Automatic Control. 45: 2383-2388. DOI: 10.1109/9.895579 |
0.586 |
|
1999 |
Abdallah CT, Ariola M, Dorato P, Koltchinskii V. Quantified inequalities and robust control Lecture Notes in Control and Information Sciences. 373-390. DOI: 10.1007/Bfb0109881 |
0.373 |
|
1994 |
Koltchinskii VI. Komlos-Major-Tusnady approximation for the general empirical process and Haar expansions of classes of functions Journal of Theoretical Probability. 7: 73-118. DOI: 10.1007/BF02213361 |
0.315 |
|
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