Convex multi-task feature learning

Aaker, D. A., Kumar, V., & Day, G. S. (2004). Marketing research (8th ed.). New York: Wiley.

Abernethy, J., Bach, F., Evgeniou, T., & Vert, J.-P. (2006). Low-rank matrix factorization with attributes (Technical Report 2006/68/TOM/DS). INSEAD, Working paper.

Ando, R. K., & Zhang, T. (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6, 1817–1853.

Argyriou, A., Micchelli, C. A., & Pontil, M. (2005). Learning convex combinations of continuously parameterized basic kernels. In Lecture notes in artificial intelligence : Vol. 3559. Proceedings of the 18th annual conference on learning theory (COLT) (pp. 338–352). Berlin: Springer.

Argyriou, A., Evgeniou, T., & Pontil, M. (2007a). Multi-task feature learning. In Schölkopf, B. Platt, J. Hoffman, T. (Eds.), Advances in neural information processing systems (Vol. 19, pp. 41–48). Cambridge: MIT Press.

Argyriou, A., Micchelli, C. A., & Pontil, M. (2007b). Representer theorems for spectral norms. Working paper, Dept. of Computer Science, University College London.

Aronszajn, N. (1950). Theory of reproducing kernels. Transactions of the American Mathematical Society, 686, 337–404.

Bakker, B., & Heskes, T. (2003). Task clustering and gating for Bayesian multi–task learning. Journal of Machine Learning Research, 4, 83–99.

Baxter, J. (2000). A model for inductive bias learning. Journal of Artificial Intelligence Research, 12, 149–198.

Ben-David, S., & Schuller, R. (2003). Exploiting task relatedness for multiple task learning. In Lecture notes in computer science : Vol. 2777. Proceedings of the 16th annual conference on learning theory (COLT) (pp. 567–580). Berlin: Springer.

Bennett, K. P., & Embrechts, M. J. (2003). An optimization perspective on partial least squares. In J. A. K. Suykens, G. Horvath, S. Basu, C. Micchelli, J. Vandewalle (Eds.), NATO science series III: computer & systems sciences : Vol. 190. Advances in learning theory: methods, models and applications (pp. 227–250). Amsterdam: IOS Press.

Bhatia, R. (1997). Matrix analysis. Springer: Graduate texts in Mathematics.

Borga, M. (1998). Learning multidimensional signal processing. PhD thesis, Dept. of Electrical Engineering, Linköping University, Sweden.

Borwein, J. M., & Lewis, A. S. (2005). CMS books in mathematics. Convex analysis and nonlinear optimization: theory and examples. Berlin: Springer.

Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

Breiman, L., & Friedman, J. H. (1997). Predicting multivariate responses in multiple linear regression. Journal of the Royal Statistical Society, Series B, 59(1), 3–54.

Caponnetto, A., & De Vito, E. (2006). Optimal rates for the regularized least-squares algorithm. Foundations of Computational Mathematics, August 2006.

Caruana, R. (1997). Multi-task learning. Machine Learning, 28, 41–75.

Chapelle, O., & Harchaoui, Z. (2005). A machine learning approach to conjoint analysis. In L. K. Saul, Y. Weiss, & L. Bottou (Eds.), Advances in neural information processing systems (Vol. 17, pp. 257–264). Cambridge: MIT Press.

Donoho, D. (2004). For most large underdetermined systems of linear equations, the minimal l1-norm near-solution approximates the sparsest near-solution. Preprint, Dept. of Statistics, Stanford University.

Evgeniou, T., Micchelli, C. A., & Pontil, M. (2005). Learning multiple tasks with kernel methods. Journal of Machine Learning Research, 6, 615–637.

Evgeniou, T., Pontil, M., & Toubia, O. (2006). A convex optimization approach to modeling consumer heterogeneity in conjoint estimation (Technical Report). INSEAD.

Fazel, M., Hindi, H., & Boyd, S. P. (2001). A rank minimization heuristic with application to minimum order system approximation. In Proceedings of the American control conference (Vol. 6, pp. 4734–4739).

Goldstein, H. (1991). Multilevel modelling of survey data. The Statistician, 40, 235–244.

Golub, G. H., & van Loan, C. F. (1996). Matrix computations. Baltimore: Johns Hopkins University Press.

Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. (2004). Canonical correlation analysis: an overview with application to learning methods. Neural Computation, 16(12), 2639–2664.

Hastie, T., Tibshirani, R., & Friedman, J. (2001). Springer series in statistics. The elements of statistical learning: data mining, inference and prediction. Berlin: Springer.

Heisele, B., Serre, T., Pontil, M., Vetter, T., & Poggio, T. (2002). Categorization by learning and combining object parts. In Advances in neural information processing systems (Vol. 14, pp. 1239–1245). Cambridge: MIT Press.

Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321–377.

Izenman, A. J. (1975). Reduced-rank regression for the multivariate linear model. Journal of Multivariate Analysis, 5, 248–264.

Jebara, T. (2004). Multi-task feature and kernel selection for SVMs. In Proceedings of the 21st international conference on machine learning.

Lawrence, N. D., & Platt, J. C. (2004). Learning to learn with the informative vector machine. In R. Greiner (Ed.), Proceedings of the international conference in machine learning. Helsinki: Omnipress.

Lenk, P. J., DeSarbo, W. S., Green, P. E., & Young, M. R. (1996). Hierarchical Bayes conjoint analysis: recovery of partworth heterogeneity from reduced experimental designs. Marketing Science, 15(2), 173–191.

Lewis, A. S. (1995). The convex analysis of unitarily invariant matrix functions. Journal of Convex Analysis, 2(1), 173–183.

Maurer, A. (2006). Bounds for linear multi-task learning. Journal of Machine Learning Research, 7, 117–139.

Micchelli, C. A., & Pinkus, A. (1994). Variational problems arising from balancing several error criteria. Rendiconti di Matematica, Serie VII, 14, 37–86.

Micchelli, C. A., & Pontil, M. (2005). On learning vector-valued functions. Neural Computation, 17, 177–204.

Neve, M., De Nicolao, G., & Marchesi, L. (2007). Nonparametric identification of population models via Gaussian processes. Automatica (Journal of IFAC), 43(7), 1134–1144.

Obozinski, G., Taskar, B., & Jordan, M. I. (2006). Multi-task feature selection (Technical report). Deptartment of Statistics, UC Berkeley, June 2006.

Poggio, T., & Girosi, F. (1998). A sparse representation for function approximation. Neural Computation, 10, 1445–1454.

Serre, T., Kouh, M., Cadieu, C., Knoblich, U., Kreiman, G., & Poggio, T. (2005). Theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex (AI Memo 2005-036). Massachusetts Institute of Technology.

Srebro, N., Rennie, J. D. M., & Jaakkola, T. S. (2005). Maximum-margin matrix factorization. In Advances in neural information processing systems (Vol. 17, pp. 1329–1336). Cambridge: MIT Press.

Torralba, A., Murphy, K. P., & Freeman, W. T. (2004). Sharing features: efficient boosting procedures for multiclass object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (Vol. 2, pp. 762–769).

Wahba, G. (1990). Series in applied mathematics : Vol. 59. Splines models for observational data. Philadelphia: SIAM.

Wold, S., Ruhe, A., Wold, H., & Dunn III, W. J. (1984). The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM Journal of Scientific Computing, 3, 735–743.

Xue, Y., Liao, X., Carin, L., & Krishnapuram, B. (2007). Multi-task learning for classification with Dirichlet process priors. Journal of Machine Learning Research, 8, 35–63.

Yu, K., Tresp, V., & Schwaighofer, A. (2005). Learning Gaussian processes from multiple tasks. In Proceedings of the 22nd international conference on machine learning.

Zhang, J., Ghahramani, Z., & Yang, Y. (2006). Learning multiple related tasks using latent independent component analysis. In Advances in neural information processing systems (Vol. 18, pp. 1585–1592). Cambridge: MIT Press.