arXiv subjects

Yinbin Han

Publications and source records attributed to Yinbin Han.

4 recordsLinked to original sources

arXiv2026

Diffusion Models for Adaptive Sequential Data Generation

Generating realistic synthetic sequential data is critical in real-world applications across operations research, finance, healthcare, energy systems, and scientific computing, where time-indexed observations are used for prediction, simulation, risk assessment, and data-driven decision-making. While diffusion models have achieved remarkable success in generating static data, their direct extensions to sequential settings often fail to capture temporal dependence and information structure. Designing diffusion models that can simulate sequential data in an adapted manner, and hence without anticipation of future information, therefore remains an open challenge. In this work, we propose a sequential forward-backward diffusion framework for adapted time series generation. Our approach progressively injects and removes noise along the sequence, conditioning on the previously generated history to ensure adaptiveness. A novel score-matching objective is introduced for efficient parallel training. We derive rigorous statistical guarantees under a generic framework, then establish score approximation, score estimation, and distribution estimation results with ReLU networks serving as a concrete instance. Empirically, we validate our method on synthetic data, including ARMA models and Gaussian processes, and demonstrate its effectiveness in constructing mean-variance optimal portfolios.

cs.LG↗

arXiv2024

Stochastic Control for Fine-tuning Diffusion Models: Optimality, Regularity, and Convergence

Diffusion models have emerged as powerful tools for generative modeling, demonstrating exceptional capability in capturing target data distributions from large datasets. However, fine-tuning these massive models for specific downstream tasks, constraints, and human preferences remains a critical challenge. While recent advances have leveraged reinforcement learning algorithms to tackle this problem, much of the progress has been empirical, with limited theoretical understanding. To bridge this gap, we propose a stochastic control framework for fine-tuning diffusion models. Building on denoising diffusion probabilistic models as the pre-trained reference dynamics, our approach integrates linear dynamics control with Kullback-Leibler regularization. We establish the well-posedness and regularity of the stochastic control problem and develop a policy iteration algorithm (PI-FT) for numerical solution. We show that PI-FT achieves global convergence at a linear rate. Unlike existing work that assumes regularities throughout training, we prove that the control and value sequences generated by the algorithm maintain the regularity. Additionally, we explore extensions of our framework to parametric settings and continuous-time formulations, and demonstrate the practical effectiveness of the proposed PI-FT algorithm through numerical experiments. Our code is available at https://github.com/yinbinhan/fine-tuning-of-diffusion-models.

cs.LG↗

arXiv2024

Neural Network-Based Score Estimation in Diffusion Models: Optimization and Generalization

Diffusion models have become a leading paradigm in generative AI, with score estimation via denoising score matching as a central component. While recent theory provides strong statistical guarantees, it typically relies on algorithm-agnostic assumptions and treats empirical risk minimization as if it were solved exactly. In practice, however, score functions are parameterized by highly nonconvex neural networks and trained by gradient descent (GD), and it remains unclear whether such practical procedures admit rigorous guarantees. We take a first step toward this question by developing a mathematical framework for score estimation with GD-trained neural networks. Our analysis addresses both optimization and generalization. We introduce a parametric formulation that reduces denoising score matching to a regression problem with noisy labels. This setting poses several challenges, including unbounded inputs, vector-valued outputs, and an additional time variable, which prevent a direct application of existing techniques. We show that, with a suitable design, the dynamics of GD-trained networks can be approximated by a sequence of localized kernel regression problems. We also show that prolonged training on noisy labels leads to overfitting, and derive an early-stopping rule adapted to unbounded domains. As a consequence, we establish the first minimax-optimal generalization bounds for GD-trained neural networks in diffusion models. Experiments on the Credit Default dataset further show that our theory-guided training framework achieves performance comparable to heavily tuned heuristic methods for generating high-fidelity financial tabular data.

cs.LG↗

arXiv2023

Policy Gradient Converges to the Globally Optimal Policy for Nearly Linear-Quadratic Regulators

Nonlinear control systems with partial information to the decision maker are prevalent in a variety of applications. As a step toward studying such nonlinear systems, this work explores reinforcement learning methods for finding the optimal policy in the nearly linear-quadratic regulator systems. In particular, we consider a dynamic system that combines linear and nonlinear components, and is governed by a policy with the same structure. Assuming that the nonlinear component comprises kernels with small Lipschitz coefficients, we characterize the optimization landscape of the cost function. Although the cost function is nonconvex in general, we establish the local strong convexity and smoothness in the vicinity of the global optimizer. Additionally, we propose an initialization mechanism to leverage these properties. Building on the developments, we design a policy gradient algorithm that is guaranteed to converge to the globally optimal policy with a linear rate.

cs.LG↗