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cs.LG2026
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…
cs.LG2025
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…
cs.LG2023
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…