Fetching the paper…
Reading the bibliography…
Zeroth-order optimization is an important research topic in machine learning.
Random optimization
Matyas, J. (1965) · 1965
Earlier work this paper cites.
The moments of products of quadratic forms in normal variables
Magnus, J. R. (1978) · 1978
Earlier work this paper cites.
Completely derandomized self-adaptation in evolution strategies
Hansen, N. & Ostermeier, A. (2001) · 2001
Earlier work this paper cites.
Dimension reduction and coefficient estimation in multivariate linear regression
Yuan, M., Ekici, A., Lu, Z., & Monteiro, R. (2007) · 2007
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
Bergstra, J. S., Bardenet, R., Bengio, Y., & Kégl, B. (2011) · 2011
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J., Hazan, E., & Singer, Y. (2011) · 2011
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · 2012
Earlier work this paper cites.
Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. & Hinton, G. (2012) · 2012
Earlier work this paper cites.
Adadelta: an adaptive learning rate method
Zeiler, M. D. (2012) · 2012
Earlier work this paper cites.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. & Lan, G. (2013) · 2013
Cited alongside, same era.
Low-rank matrix factorization for deep neural network training with high-dimensional output targets
Sainath, T. N., Kingsbury, B., Sindhwani, V., Arisoy, E., & Ramabhadran, B. (2013) · 2013
Cited alongside, same era.
Natural evolution strategies
Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J., & Schmidhuber, J. (2014) · 2014
Cited alongside, same era.
Optimal rates for zero-order convex optimization: The power of two function evaluations
Duchi, J. C., Jordan, M. I., Wainwright, M. J., & Wibisono, A. (2015) · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J. (2015) · 2015
Cited alongside, same era.
Highly-smooth zero-th order online optimization
Bach, F. & Perchet, V. (2016) · 2016
Generating adversarial malware examples for black-box attacks based on gan
Hu, W. & Tan, Y. (2017) · 2017
Later among the works it cites.
Random gradient-free minimization of convex functions
Nesterov, Y. & Spokoiny, V. (2017) · 2017
Later among the works it cites.
Practical black-box attacks against machine learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017) · 2017
Later among the works it cites.
Evolution strategies as a scalable alternative to reinforcement learning
Salimans, T., Ho, J., Chen, X., Sidor, S., & Sutskever, I. (2017) · 2017
Later among the works it cites.
An optimal algorithm for bandit and zero-order convex optimization with two-point feedback
Shamir, O. (2017) · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
An improved gap-dependency analysis of the noisy power method
Balcan, M.-F., Du, S. S., Wang, Y., & Yu, A. W. (2016) · 2016
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. & Wagner, D. (2017) · 2017
Cited alongside, same era.
Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Chen, P.-Y., Zhang, H., Sharma, Y., Yi, J., & Hsieh, C.-J. (2017) · 2017
Cited alongside, same era.
Understanding and exploiting the low-rank structure of deep networks
Bakker, C., Henry, M. J., & Hodas, N. O. (2018) · 2018
Closest in time.
Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator
Fang, C., Li, C. J., Lin, Z., & Zhang, T. (2018) · 2018
Closest in time.
Black-box adversarial attacks with limited queries and information
Ilyas, A., Engstrom, L., Athalye, A., & Lin, J. (2018) · 2018
Closest in time.
Zeroth-order stochastic variance reduction for nonconvex optimization
Liu, S., Kailkhura, B., Chen, P.-Y., Ting, P., Chang, S., & Amini, L. (2018) · 2018
Closest in time.