Publications

Pre-prints:

  • “Generalization Error Bounds with Probabilistic Guarantee for SGD in Nonconvex Optimization”. Y. Zhou, Y. Liang. ArXiv:1802.06903v1.

  • “Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization”. Z. Wang,  Y. Zhou, Y. Liang. ArXiv:1802.07372v1.

  • “Low-rank Matrix Recovery under Non-uniform Error Corruption and Missing Observation”. H. Zhang, Y. Zhou, Y. Liang. Submitted to IEEE Transactions on Information Theory.

2018

  • “Convergence of Cubic Regularization for Nonconvex Optimization under KŁ Property”. Y. Zhou, W. Zhe and Y. Liang. To appear in Neural Information Processing Systems (NIPS), 2018.

  • “Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters”. Y. Zhou, Y. Yu, W. Dai, Y. Liang, E. Xing. To appear in Journal of Machine Learning Research.

  • “Critical Points of Neural Networks: Analytical Forms and Landscape Properties”. Y. Zhou, Yingbin Liang. In Proc. International Conference on Learning Representations (ICLR), 2018.

2017

  • “A Nonconvex Approach for Phase Retrieval: Reshaped Wirtinger Flow and Incremental Algorithms”. H. Zhang, Y. Zhou, Y. Liang, and Y. Chi. Journal of Machine Learning Research, 18(141), 2017.

  •  “Characterization of Gradient Dominance and Regularity Conditions for Neural Networks”. Y. Zhou, Yingbin Liang. In Proc. Neural Information Processing Systems (NIPS) workshop on deep learning theory, 2017.

  •  “Analyzable Diversity-Promoting Latent Space Models”. P. Xie, Y. Deng, Y. Zhou, A. Kumar, Y. Yu, J. Zou, E. P. Xing. In Proc. International Conference on Machine Learning (ICML), 2017 (Oral Presentation).

  • “Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization”. Q. Li, Y. Zhou, Y. Liang. In Proc. International Conference on Machine Learning (ICML), 2017.

  • “Demixing Sparse Signals via Convex Optimization”. Y. Zhou, Y. Liang. In Proc. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2017.

2016

  • “Accelerated Gradient Descent for Non-convex Phase Retrieval”. Y. Zhou, H. Zhang, Y. Liang. In Proc. Annual Allerton Conference on Communication, Control, and Computing, 2016.

  • “On Compressive Orthonormal Sensing”. Y. Zhou, H. Zhang, Y. Liang. In Proc. Annual Allerton Conference on Communication, Control, and Computing, 2016.

  • “Lighter-Communication Distributed Machine  Learning via Sufficient Factor Broadcasting”. P. Xie, J. Kim, Y. Zhou, Q. Ho, A. Kumar, Y. Yu, E. Xing. In Proc. Uncertainty in Artificial Intelligence (UAI), 2016.

  • “On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System”. Y. Zhou, Y. Yu, W. Dai, Y. Liang, E. Xing. In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.

2015

  • “Analysis of Robust PCA via Local Incoherence”. H. Zhang, Y. Zhou, Y. Liang. In Proc. Neural Information Processing Systems (NIPS), 2015.

2013

  • “Asymmetric-access Aware Optimization for STT-RAM Caches with Process Variations”. Y. Zhou, C. Zhang, G. Sun, K. Wang, Y. Zhang. In Proc. Great Lakes Symposium on VLSI (GLSVLSI), 2013.