3 papers
cs.LG2025
Towards Reliable Time Series Forecasting under Future Uncertainty: Ambiguity and Novelty Rejection Mechanisms
Ninghui Feng, Songning Lai, Xin Zhou +9
In real-world time series forecasting, uncertainty and lack of reliable evaluation pose significant challenges. Notably, forecasting errors often arise from underfitting in-distrib…
cs.CR2024
Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models
Songning Lai, Yu Huang, Jiayu Yang +3
The increasing complexity of AI models, especially in deep learning, has raised concerns about transparency and accountability, particularly in high-stakes applications like medica…
cs.LG2024
Learning New Concepts, Remembering the Old: Continual Learning for Multimodal Concept Bottleneck Models
Songning Lai, Mingqian Liao, Zhangyi Hu +6
Concept Bottleneck Models (CBMs) enhance the interpretability of AI systems, particularly by bridging visual input with human-understandable concepts, effectively acting as a form…