Learning the Step-size Policy for the Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm

D. C. Liu and J. Nocedal, “On the limited memory BFGS method for large scale optimization,” Mathematical programming

1989

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.

Springer Science & Business Media, 1998

S. Thrun and L. Pratt, Learning to learn · 1998

Earlier work this paper cites.

Y. Bengio, “Gradient-based optimization of hyperparameters,” Neural computation

2000

Earlier work this paper cites.

S. Hochreiter, A. S. Younger, and P. R. Conwell, “Learning to learn using gradient descent,” in International Conference on Artificial Neural Networks

2001

Earlier work this paper cites.

Springer Science & Business Media, 2006

J. Nocedal and S. Wright, Numerical optimization · 2006

Earlier work this paper cites.

A. Krizhevsky, G. Hinton, et al

2009

Earlier work this paper cites.

J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, “Algorithms for hyper-parameter optimization,” in Advances in neural information processing systems

2011

Earlier work this paper cites.

J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” The Journal of Machine Learning Research

2012

Cited alongside, same era.

M. D. Zeiler, “Adadelta: an adaptive learning rate method,” arXiv preprint arXiv:1212.5701

Original

2012

Cited alongside, same era.

J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. Patwary, M. Prabhat, and R. Adams, “Scalable bayesian optimization using deep neural networks,” in International Conference on Machine Learning

2015

Cited alongside, same era.

C. Daniel, J. Taylor, and S. Nowozin, “Learning step size controllers for robust neural network training.,” in AAAI

2016

Cited alongside, same era.

K. Li and J. Malik, “Learning to optimize,” arXiv preprint arXiv:1606.01885

Original

C. Zhou, W. Gao, and D. Goldfarb, “Stochastic adaptive quasi-newton methods for minimizing expected values,” in International Conference on Machine Learning

2017

Later among the works it cites.

X. Dong, J. Shen, W. Wang, Y. Liu, L. Shao, and F. Porikli, “Hyperparameter optimization for tracking with continuous deep Q-learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition

2018

Later among the works it cites.

R. Bollapragada, D. Mudigere, J. Nocedal, H.-J. M. Shi, and P. T. P. Tang, “A progressive batching L-BFGS method for machine learning,” arXiv preprint arXiv:1802.05374

Original

2018

Later among the works it cites.

L. Metz, N. Maheswaranathan, J. Nixon, D. Freeman, and J. Sohl-Dickstein, “Understanding and correcting pathologies in the training of learned optimizers,” in International Conference on Machine Learning

2019

Later among the works it cites.