Publications

• Chris Cannella & Jie Ding, Mohammadreza Soltani, Yi Zhou, Vahid Tarokh (2020). Perception-Distortion Trade-off with Restricted Boltzmann Machines. ICASSP. Published, 12/01/2020.
• Kaiyi Ji & Zhe Wang, Bowen Weng, Yi Zhou, Wei Zhang, Yingbin Liang (2020). History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms. ICML 2020. Published, 12/01/2020.
• Yi Zhou & Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh (2020). Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization. IJCAI2020. Published, 12/01/2020.
• Cat P. Le & Yi Zhou, Jie Ding, Vahid Tarokh (2020). SUPERVISED ENCODING FOR DISCRETE REPRESENTATION LEARNING. ICASSP. Published, 12/01/2020.
• Tengyu Xu, Zhe Wang, Yi Zhou & Yingbin Liang (2020). Reanalysis of Variance Reduced Temporal Difference Learning. International Conference on Learning Representations (ICLR). Published, 12/01/2020.
• Cheng Chen & Junjie Yang, Yi Zhou (2020). Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. IEEE Bigdata 2020. Published, 12/01/2020.
• Shaocong Ma & Yi Zhou, Shaofeng Zou (2020). Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. NeurIPS 2020. Published, 12/01/2020.
• Bhavya Kailkhura & Yi Zhou (2020). A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning. NeurIPS 2020. Published, 12/01/2020.
• Shaocong Ma & Yi Zhou (2020). Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. ICML 2020. Published, 12/01/2020.
• Cheng Chen & Ziyi Chen, Yi Zhou, Bhavya Kailkhura (2020). FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. IEEE Bigdata 2020. Published, 12/01/2020.
• Zhe Wang, Yi Zhou, Yingbin Liang & Guanghui Lan (2019). Cubic Regularization with Momentum for Nonconvex Optimization. Uncertainty in AI (UAI). Published, 12/01/2019.
• Zhe Wang, Yi Zhou & Yingbin Liang (2019). Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization. Artificial Intelligence and Statistics (AISTATS). Published, 12/01/2019.
• Yi Zhou, Junjie Yang, Huishuai Zhang & Yingbin Liang (2019). SGD Converges to Global Minimum in Deep Learning via Star- convex Path. International Conference on Learning Representations (ICLR). Published, 12/01/2019.
• Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang & Eric Xing (2019). Toward Understanding the Impact of Staleness in Distributed Machine Learning. International Conference on Learning Representations (ICLR). Published, 12/01/2019.
• Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang & Vahid Tarokh (2019). SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization. Neural Information Processing Systems (NeurIPS). Published, 12/01/2019.
• Yi Zhou, Wang Zhe & Yingbin Liang (2019). Convergence of Cubic Regularization for Nonconvex Optimization under KŁ Property. Neural Information Processing Systems (NeurIPS),. Published, 12/01/2019.
• J. Regatti, G. Tendolkar, Y. Zhou, A. Gupta & Y. Liang (2019). Distributed SGD Generalizes Well Under Asynchrony. Annual Allerton Conference. Published, 12/01/2019.
• Y. Feng, Y. Zhou & V. Tarokh (2019). Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. IEEE Bigdata conference. Published, 12/01/2019.
• Y. Zhou, Y. Feng, V. Tarokh, V. Gintautas, J. Mcclelland & D. Garagic (2019). Multi-level Mean-shift Clustering for Single-channel Radio Frequency Signal Separation. Machine Learning for Signal Processing (MLSP). Published, 12/01/2019.
• Zhe Wang, Yi Zhou, Yingbin Liang & Guanghui Lan (2019). A Note on Inexact Condition for CubicRegularized Newton’s Method. Operations Research Letters. Published, 08/01/2019.
• Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang & Vahid Tarokh (2019). Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. International Conference on Machine Learning (ICML). Published, 07/01/2019.

Presentations

• Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization (IJCAI). Conference Paper, Refereed, Presented, 01/15/2021.
• Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study. Cheng Chen, Junjie Yang, Yi Zhou. IEEE Bigdata 2020. Conference Paper, Refereed, Presented, 12/10/2020.
• FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling. Cheng Chen, Ziyi Chen, Yi Zhou, Bhavya Kailkhura. IEEE Bigdata 2020. Conference Paper, Refereed, Presented, 12/10/2020.
• Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis. Shaocong Ma, Yi Zhou, Shaofeng Zou. NeurIPS 2020. Conference Paper, Refereed, Presented, 12/06/2020.
• Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle. Shaocong Ma, Yi Zhou. ICML 2020. Conference Paper, Refereed, Presented, 06/30/2020.
• Present ``SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization'' and ``Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization'' at NeurIPS 2019. Conference Paper, Refereed, Presented, 12/15/2019.
• Present ``SGD Converges to Global Minimum in Deep Learning via Star- convex Path'' at ICLR 2019. Conference Paper, Refereed, Presented, 05/06/2019.

Research Groups

• Ziyi Cheng, Graduate Student. ECE/Ph.D.. 08/15/2019 - present.
• Cheng Chen, Graduate Student. ECE/Ph.D.. 08/15/2019 - present.
• Shaocong Ma, Graduate Student. ECE/Ph.D.. 08/15/2019 - present.