Fetching the paper…
Reading the bibliography…
We present a framework to derive risk bounds for vector-valued learning with a broad class of feature maps and loss functions.
D. Slepian. The one-sided barrier problem for Gaussian noise. Bell System Tech. J
1962
Earlier work this paper cites.
M. Ledoux, M. Talagrand. Probability in Banach Spaces: Isoperimetry and Processes
1991
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE
1998
Earlier work this paper cites.
M. Anthony and P. L. Bartlett. Neural network learning: Theoretical foundations
1999
Earlier work this paper cites.
S. Haykin. Neural Networks: A Comprehensive Foundation
1999
Earlier work this paper cites.
J. Baxter. A model of inductive bias learning. Journal of Artificial Intelligence Research
2000
Earlier work this paper cites.
P. L. Bartlett and S. Mendelson. Rademacher and Gaussian Complexities: Risk bounds and structural results. Journal of Machine Learning Research
2002
Earlier work this paper cites.
K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research
2002
Earlier work this paper cites.
V. Koltchinskii and D. Panchenko. Empirical margin distributions and bounding the generalization error of combined classifiers. Annals of Statistics , 30(1):1–50, 2002
2002
Cited alongside, same era.
R. Meir and T. Zhang. Generalization error bounds for Bayesian mixture algorithms. Journal of Machine Learning Research , 4:839–860, 2003
2003
Cited alongside, same era.
R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research , 6, 1817–1853, 2005
2005
Cited alongside, same era.
In Proceedings of the 18th Annual Conference on Learning Theory , pages 545–560, 2005
N. Srebro and A. Shraibman. Rank, trace-norm and max-norm · 2005
Cited alongside, same era.
A. Maurer. Bounds for linear multi-task learning. Journal of Machine Learning Research , 7:117–139, 2006
Y. Mroueh, T., Poggio, R. Rosasco, and J. Slotine. Multiclass learning with simplex coding. In Advances in Neural Information Processing Systems
2012
Later among the works it cites.
S. Boucheron, G. Lugosi, and P. Massart. Concentration Inequalities
2013
Later among the works it cites.
A. Maurer, and M. Pontil. Excess risk bounds for multitask learning with trace norm regularization. In Proceeding of the 26th Annual Conference on Learning Theory
2013
Later among the works it cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the 2014 Conference on Computer Vision and Pattern Recognition , pages 580–587, 2014
2014
Later among the works it cites.
A. Maurer. A chain rule for the expected suprema of Gaussian processes. In Proceedings of the 25th International Conference on Algorithmic Learning Theory
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2006
Cited alongside, same era.
Y. Amit, M. Fink, N. Srebro, and S. Ullman. Uncovering shared structures in multiclass classification. In Proceedings of the 24th international conference on Machine learning
2007
Cited alongside, same era.
G. Cavallanti, N. Cesa-Bianchi, and C. Gentile. Linear algorithms for online multitask classification. Journal of Machine Learning Research , 11:2597–2630, 2010
2010
Cited alongside, same era.
S. M. Kakade, S. Shalev-Shwartz, A. Tewari. Regularization techniques for learning with matrices. Journal of Machine Learning Research
2012
Cited alongside, same era.
Cited in the paper.
Cited in the paper.
2014
Later among the works it cites.
A. Maurer, M. Pontil, and B. Romera-Paredes. An inequality with applications to structured sparsity and multitask dictionary learning. In Proceedings of the 27th Conference on Learning Theory
2014
Later among the works it cites.
Y. Lei, U., Dogan, A. Binder, and M. Kloft. Multi-class SVMs: From tighter data-dependent generalization bounds to novel algorithms. In Advances in Neural Information Processing Systems
2015
Later among the works it cites.
B. Neyshabur, R. Tomioka, and N. Srebro. Norm-based capacity control in neural networks. In Proceedings of the 28th Conference on Learning Theory
2015
Later among the works it cites.