6 papers
Online Riemannian Gradient Descent for Quantum State Tomography with Matrix Product Operators
Jian-Feng Cai, Jingyang Li, Xiaoqun Zhang +1
Matrix product operators (MPOs) provide a scalable approach for quantum state tomography (QST) by offering a compact representation of many-body mixed states with limited entanglem…
Uncertainty Quantification for Noisy Low-tubal-rank Tensor Completion
Jiuqian Shang, Jingyang Li, Yang Chen
High-dimensional tensor data often exhibit strong temporal correlations that appear as low-dimensional structures in the frequency domain. While the low-tubal-rank tensor model eff…
Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
Kuangyu Ding, Jingyang Li, Kim-Chuan Toh
Stochastic gradient methods for minimizing nonconvex composite objective functions typically rely on the Lipschitz smoothness of the differentiable part, but this assumption fails…
Online Policy Learning and Inference by Matrix Completion
Congyuan Duan, Jingyang Li, Dong Xia
Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences…
Federated PCA and Estimation for Spiked Covariance Matrices: Optimal Rates and Efficient Algorithm
Jingyang Li, T. Tony Cai, Dong Xia +1
Federated Learning (FL) has gained significant recent attention in machine learning for its enhanced privacy and data security, making it indispensable in fields such as healthcare…
Online Tensor Learning: Computational and Statistical Trade-offs, Adaptivity and Optimal Regret
Jingyang Li, Jian-Feng Cai, Yang Chen +1
Large tensor learning algorithms are typically computationally expensive and require storing a vast amount of data. In this paper, we propose a unified online Riemannian gradient d…