1 citations · 3 across the 8 of their papers we have counts for
8 papers
Minimax Optimality of Score-Entropy Discrete Diffusion
Cholyeon Cho, Yuchen Wu
Discrete diffusion models have demonstrated strong performance across a range of datasets, including natural language data and graph-structured data. Among many variants, score-ent…
On the Robustness of Distribution Support under Diffusion Guidance
Ruijia Cao, Yuchen Wu, Nisha Chandramoorthy
Diffusion guidance is a powerful technique that enables controllable and high-fidelity sample generation with diffusion models. At a high level, it modifies the score function by i…
Provably Reliable Classifier Guidance via Cross-Entropy Control
Sharan Sahu, Arisina Banerjee, Yuchen Wu
Classifier-guided diffusion models generate conditional samples by augmenting the reverse-time score with the gradient of the log-probability predicted by a probabilistic classifie…
LLP: LLM-Based Product Pricing in E-commerce
Hairu Wang, Sheng You, Qiheng Zhang +5
Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) often face significant challenges i…
Interpretable Droplet Digital PCR Assay for Trustworthy Molecular Diagnostics
Yuanyuan Wei, Yucheng Wu, Fuyang Qu +5
Accurate molecular quantification is essential for advancing research and diagnostics in fields such as infectious diseases, cancer biology, and genetic disorders. Droplet digital…
Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models
Yuchen Wu, Yuxin Chen, Yuting Wei
Diffusion models play a pivotal role in contemporary generative modeling, claiming state-of-the-art performance across various domains. Despite their superior sample quality, mains…
Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
Yuchen Wu, Minshuo Chen, Zihao Li +2
Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. Such information is coine…
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models
Song Mei, Yuchen Wu
We investigate the approximation efficiency of score functions by deep neural networks in diffusion-based generative modeling. While existing approximation theories utilize the smo…