Proximal regularization for online and batch learning

ACM International Conference Proceeding Series

Volumn 382, Issue , 2009, Pages

  Do, Chuong B ^a   Le, Quoc V ^a   Foo, Chuan Sheng ^b  

^a Stanford University   (United States)

^b INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

BATCH ALGORITHMS; BATCH LEARNING; BUNDLE METHODS; FAST CONVERGENCE; LEARNING PROBLEM; LOGISTIC REGRESSIONS; OBJECTIVE FUNCTIONS; ON-LINE SETTING; OPTIMAL CHOICE; OPTIMIZATION TASK; REGULARIZATION PARAMETERS; STRUCTURED LEARNING; STRUCTURED PREDICTION; THEORETICAL PERFORMANCE;

CONVERGENCE OF NUMERICAL METHODS; CURVE FITTING; EDUCATION; MATHEMATICAL MODELS; ROBOT LEARNING; SUPPORT VECTOR MACHINES;

LEARNING ALGORITHMS;

EID: 70049104379     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553407     Document Type: Conference Paper

References (14)

1. Abernethy, J., Bartlett, P. L., Rakhlin, A., & Tewari, A. (2008). Optimal strategies and minimax lower bounds for online convex games. Proceedings of the 21st Annual Conference on Computational Learning Theory.
2. Bartlett, P., Hazan, E., & Rakhlin, A. (2008). Adaptive online gradient descent. In J. Platt, D. Koller, Y. Singer and S. Roweis (Eds.), Advances in Neural Information Processing Systems 20, 65-72. MIT Press.
3. Chapelle, O., Le, Q. V., & Smola, A. J. (2007). Large margin optimization of ranking measures. NIPS Workshop: Machine Learning for Web Search.
4. Do, C. B., Woods, D. A., & Batzoglou, S. (2006). CONTRAfold: RNA secondary structure prediction without physics-based models. Bioinformatics, 22, e90-e98.
5. Hazan, E., Agarwal, A., & Kale, S. (2007). Logarithmic regret algorithms for online convex optimization. Mach Learn, 69, 169-192.
6. Joachims, T. (2006). Training linear SVMs in linear time. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 217-226).
7. Kiwiel, K. C. (1983). Proximity control in bundle methods for convex nondifferentiable minimization. Math Program, 27, 320-341.
8. Lemare´chal, C., Nemirovskii, A., & Nesterov, Y. (1995). New variants of bundle methods. Math Program, 69, 111-147.
9. Schramm, H., & Zowe, J. (1992). A version of the bundle idea for minimizing a nonsmooth function: conceptual idea, convergence analysis, numerical results. SIAM J Optim, 2, 121-152.
10. Shalev-Shwartz, S., & Kakade, S. M. (2009). Mind the duality gap: Logarithmic regret algorithms for online optimization. In D. Koller, D. Schuurmans, Y. Bengio and L. Bottou (Eds.), Advances in Neural Information Processing Systems 21, 1457-1464. MIT Press.
11. Shalev-Shwartz, S., Singer, Y., & Srebro, N. (2007). Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. Proceedings of the 24th Annual International Conference on Machine Learning (pp. 807-814).
12. Smola, A., Vishwanathan, S. V. N., & Le, Q. (2008). Bundle methods for machine learning. In J. Platt, D. Koller, Y. Singer and S. Roweis (Eds.), Advances in Neural Information Processing Systems 20, 1377-1384. MIT Press.
13. Teo, C. H., Smola, A., Vishwanathan, S. V., & Le, Q. V. (2007). A scalable modular convex solver for regularized risk minimization. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 727-736).
14. Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. Proceedings of the 20th Annual International Conference on Machine Learning.