6 papers
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…
Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models
Eric Hanchen Jiang, Mengting Li, Guancheng Wan +8
The efficiency of multi-agent systems driven by large language models (LLMs) largely hinges on their communication topology. However, designing an optimal topology is a non-trivial…
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…