1. Allen-Zhu, Z., Li, Y., and Liang, Y. (2019), “Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers,” in Advances in Neural Information Processing Systems (Vol. 32), eds. H. Wallach, H. Larochelle, A. Beygelzimer, F. d’ Alché-Buc, E. Fox, and R. Garnett, pp. 6158–6169. Curran Associates, Inc.
2. Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y. (2017), “Generalization and Equilibrium in Generative Adversarial Nets (GANs),” in Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, eds. D. Precup and Y. W. Teh, pp. 224–232, PMLR.
3. Arpit, D., Jastrzundefinedbski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., and Lacoste-Julien, S. (2017), “A Closer Look at Memorization in Deep Networks,” in Proceedings of the 34th International Conference on Machine Learning - Volume 70, ICML’17, pp. 233–242. JMLR.org.
4. Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
5. A similarity-based QSAR model for predicting acute toxicity towards the fathead minnow (Pimephales promelas)