17 citations · 39 across the 17 of their papers we have counts for
7 papers · 1 filter
Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices
Changxiao Cai, Yuchen Jiao, Gen Li
Diffusion models are known to exploit unknown low-dimensional structure to accelerate sampling. However, existing convergence theory under low-dimensional data structure has largel…
Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions
Jingda Wu, Changxiao Cai
Score-based diffusion models have demonstrated remarkable empirical success in learning high-dimensional distributions, particularly those exhibiting low-dimensional and multi-moda…
Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach
Yuchen Jiao, Na Li, Changxiao Cai +1
Higher-order ODE solvers have become a standard tool for accelerating diffusion probabilistic model (DPM) sampling, motivating the widespread view that first-order methods are inhe…
Minimax-optimal trust-aware multi-armed bandits
Changxiao Cai, Jiacheng Zhang
Multi-armed bandit (MAB) algorithms have achieved significant success in sequential decision-making applications, under the premise that humans perfectly implement the recommended…
Transfer Learning for Contextual Multi-armed Bandits
Changxiao Cai, T. Tony Cai, Hongzhe Li
Motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, wher…
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
Gen Li, Changxiao Cai, Yuxin Chen +2
Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of reinforcement learning. When it comes to th…
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Changxiao Cai, H. Vincent Poor, Yuxin Chen
We study the distribution and uncertainty of nonconvex optimization for noisy tensor completion -- the problem of estimating a low-rank tensor given incomplete and corrupted observ…