时   间：2026年6月11 日16：00 - 17：00

地   点：中北理科大楼A1514室

报告人：Yanfeng Yang日本综合研究大学院大学博士生

主持人：谌自奇  华东师范大学

摘   要：

Creating large-scale datasets for training high-performance generative models is often prohibitively expensive, especially when associated attributes or annotations must be provided. As a result, merging existing datasets has become a common strategy. However, the sets of attributes across datasets are often inconsistent, and their naive concatenation typically leads to block-wise missing conditions. This presents a significant challenge for conditional generative modeling when the multiple attributes are used jointly as conditions, thereby limiting the model’s controllability and applicability. To address this issue, we propose a novel generative approach, Diffusion Model with Double Guidance, which enables precise conditional generation even when no training samples contain all conditions simultaneously. Our method maintains rigorous control over multiple conditions without requiring joint annotations. We demonstrate its effectiveness in molecular and image generation tasks, where it outperforms existing baselines both in alignment with target conditional distributions and in controllability under missing condition settings.

报告人简介：

杨彦丰2024年硕士毕业于bevictor伟德官网统计专业，并开始就读于综合研究大学院大学统计专业，师从谌自奇以及Kenji Fukumizu。主要研究方向为条件生成模型、因果推断、时间序列建模等。在Neurips, AAAI, ICLR发表论文数篇。