Statistical Optimality of Interpolated Nearest Neighbor Algorithms

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014), “Dropout: a simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research

1958

Earlier work this paper cites.

Cover, T. M. (1968), “Rates of convergence for nearest neighbor procedures,” in Proceedings of the Hawaii International Conference on Systems Sciences

1968

Earlier work this paper cites.

Wagner, T. (1971), “Convergence of the nearest neighbor rule,” IEEE Transactions on Information Theory

1971

Earlier work this paper cites.

Fritz, J. (1975), “Distribution-free exponential error bound for nearest neighbor pattern classification,” IEEE Transactions on Information Theory

1975

Earlier work this paper cites.

Tsybakov, A. B. (2004), “Optimal aggregation of classifiers in statistical learning,” Annals of Statistics

2004

Earlier work this paper cites.

Audibert, J.-Y. and Tsybakov, A. B. (2007), “Fast learning rates for plug-in classifiers,” The Annals of statistics

2007

Earlier work this paper cites.

Sun, L. and Hong, L. J. (2009), “A general framework of importance sampling for value-at-risk and conditional value-at-risk,” in Winter Simulation Conference

2009

Cited alongside, same era.

— (2010), “Asymptotic representations for importance-sampling estimators of value-at-risk and conditional value-at-risk,” Operations Research Letters

2010

Cited alongside, same era.

Schoenmakers, J., Zhang, J., and Huang, J. (2013), “Optimal dual martingales, their analysis, and application to new algorithms for Bermudan products,” SIAM Journal on Financial Mathematics

2013

Cited alongside, same era.

Chaudhuri, K. and Dasgupta, S. (2014), “Rates of convergence for nearest neighbor classification,” in Advances in Neural Information Processing Systems

2014

Cited alongside, same era.

Chen, X.-W. and Lin, X. (2014), “Big data deep learning: challenges and perspectives,” IEEE access

Gal, Y. and Ghahramani, Z. (2016), “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning

2016

Later among the works it cites.

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. (2016), “Deep learning for visual understanding: A review,” Neurocomputing

2016

Later among the works it cites.

Lyon, R. J., Stappers, B., Cooper, S., Brooke, J., and Knowles, J. (2016), “Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach,” Monthly Notices of the Royal Astronomical Society

2016

Later among the works it cites.

Sun, W. W., Qiao, X., and Cheng, G. (2016), “Stabilized nearest neighbor classifier and its statistical properties,” Journal of the American Statistical Association

2016

Later among the works it cites.