most citedConvergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization

36 citations · 97 across the 5 of their papers we have counts for

collaborators

6 papers

cs.IT2018

Secrecy Capacity of Colored Gaussian Noise Channels with Feedback

Chong Li, Yingbin Liang, H. Vincent Poor +1

In this paper, the k-th order autoregressive moving average (ARMA(k)) Gaussian wiretap channel with noiseless causal feedback is considered, in which an eavesdropper receives noisy…

stat.ML201730 cited

Critical Points of Neural Networks: Analytical Forms and Landscape Properties

Yi Zhou, Yingbin Liang

Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural netwo…

stat.ML201726 cited

Characterization of Gradient Dominance and Regularity Conditions for Neural Networks

Yi Zhou, Yingbin Liang

The past decade has witnessed a successful application of deep learning to solving many challenging problems in machine learning and artificial intelligence. However, the loss func…

cs.IT20175 cited

Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent

Yuanxin Li, Yuejie Chi, Huishuai Zhang +1

Recent work has demonstrated the effectiveness of gradient descent for directly recovering the factors of low-rank matrices from random linear measurements in a globally convergent…

cs.LG201736 cited

Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization

Qunwei Li, Yi Zhou, Yingbin Liang +1

In many modern machine learning applications, structures of underlying mathematical models often yield nonconvex optimization problems. Due to the intractability of nonconvexity, t…

cs.IT2017

State-Dependent Gaussian Multiple Access Channels: New Outer Bounds and Capacity Results

Wei Yang, Yingbin Liang, Shlomo Shamai +1

This paper studies a two-user state-dependent Gaussian multiple-access channel (MAC) with state noncausally known at one encoder. Two scenarios are considered: i) each user wishes…