Xiaowei Gao

NeurIPS Conference 2025 Conference Paper

Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking

• Pengxiang Li
• Shilin Yan
• Jiayin Cai
• Renrui Zhang
• Ruichuan An
• Ziyu Guo
• Xiaowei Gao

Classifier-Free Guidance (CFG) significantly enhances controllability in generative models by interpolating conditional and unconditional predictions. However, standard CFG often employs a static unconditional input, which can be suboptimal for iterative generation processes where model uncertainty varies dynamically. We introduce Adaptive Classifier-Free Guidance (A-CFG), a novel method that tailors the unconditional input by leveraging the model's instantaneous predictive confidence. At each step of an iterative (masked) diffusion language model, A-CFG identifies tokens in the currently generated sequence for which the model exhibits low confidence. These tokens are temporarily re-masked to create a dynamic, localized unconditional input. This focuses CFG's corrective influence precisely on areas of ambiguity, leading to more effective guidance. We integrate A-CFG into a state-of-the-art masked diffusion language model and demonstrate its efficacy. Experiments on diverse language generation benchmarks show that A-CFG yields substantial improvements over standard CFG, achieving, for instance, a 3. 9 point gain on GPQA. Our work highlights the benefit of dynamically adapting guidance mechanisms to model uncertainty in iterative generation.

YNIMG Journal 2025 Journal Article

NaDyNet: A toolbox for dynamic network analysis of naturalistic stimuli

• Junjie Yang
• Zhe Hu
• Junjing Li
• Xiaolin Guo
• Xiaowei Gao
• Jiaxuan Liu
• Yaling Wang
• Zhiheng Qu

AAAI Conference 2025 Conference Paper

Time Series Supplier Allocation via Deep Black-Litterman Model

• Xinke Jiang
• Wentao Zhang
• Yuchen Fang
• Xiaowei Gao
• Hao Chen
• Haoyu Zhang
• Dingyi Zhuang
• Jiayuan Luo

As a typical problem of Spatiotemporal Resource Management, Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy the trade-off between demands and maximum supply. The Black-Litterman (BL) model, which comes from financial portfolio management, offers a new perspective for the TSSA by balancing expected returns against insufficient supply risks. However, the BL model is not only constrained by manually constructed perspective matrices and spatio-temporal market dynamics but also restricted by the absence of supervisory signals and unreliable supplier data. To solve these limitations, we introduce the pioneering Deep Black-Litterman Model for TSSA, which innovatively adapts the BL model from financial domain to supply chain context. Specifically, DBLM leverages Spatio-Temporal Graph Neural Networks (STGNNs) to capture spatio-temporal dependencies for automatically generating future perspective matrices. Moreover, a novel Spearman rank correlation is designed as our DBLM supervise signal to navigate complex risks and interactions of the supplier. Finally, DBLM further uses a masking mechanism to counteract the bias of unreliable data, thus improving precision and reliability. Extensive experiments on two datasets demonstrate significant improvements of DBLM on TSSA.