3 papers
cs.LG2025
Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization
Langzhou He, Junyou Zhu, Fangxin Wang +5
Molecular foundation models are rapidly advancing scientific discovery, but their unreliability on out-of-distribution (OOD) samples severely limits their application in high-stake…
cs.LG2025
DiffPuter: Empowering Diffusion Models for Missing Data Imputation
Hengrui Zhang, Liancheng Fang, Qitian Wu +1
Generative models play an important role in missing data imputation in that they aim to learn the joint distribution of full data. However, applying advanced deep generative models…
cs.LG2024
Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data
Hengrui Zhang, Liancheng Fang, Qitian Wu +1
Autoregressive models are predominant in natural language generation, while their application in tabular data remains underexplored. We posit that this can be attributed to two fac…