2 citations · 2 across the 5 of their papers we have counts for
5 papers
Variable-Rate Deep Image Compression based on Low-Rank Adaptation by Progressive Learning
Xing-Yu Xu, Chen-Hsiu Huang, Ja-Ling Wu
In the digital age, image compression is crucial for numerous applications, including web media, streaming services, high-resolution medical imaging, and connected vehicle networks…
Polynomial Convergence of Riemannian Diffusion Models
Xingyu Xu, Ziyi Zhang, Yorie Nakahira +2
Diffusion models have demonstrated remarkable empirical success in the recent years and are considered one of the state-of-the-art generative models in modern AI. These models cons…
Provably Efficient and Agile Randomized Q-Learning
He Wang, Xingyu Xu, Yuejie Chi
While Bayesian-based exploration often demonstrates superior empirical performance compared to bonus-based methods in model-based reinforcement learning (RL), its theoretical under…
Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction
Xingyu Xu, Yuejie Chi
In a great number of tasks in science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing cer…
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization
Cong Ma, Xingyu Xu, Tian Tong +1
Many problems encountered in science and engineering can be formulated as estimating a low-rank object (e.g., matrices and tensors) from incomplete, and possibly corrupted, linear…