提出一种自适应学习机制，让模型在数据缺失时仍能稳定训练。
Closing the loop in learning with missing data
- 从动力系统视角建模数据缺失，设计带李雅普诺夫稳定性的更新调节机制
- 在间歇性观测下保持学习一致性，实现残差与更新几何的有界控制
- 适用于多模态、极端稀疏场景，适合研究数据不完整问题的学者
当训练数据缺失时，机器学习模型应如何学习？本文从动力系统角度出发，将数据缺失视为结构化执行失效，限制参数误差动态的可控性，并推导出具有李雅普诺夫稳定特性的自适应机制，通过调节模型更新以维持部分、间歇可观测条件下的学习一致性。在周期性激励下，分析给出了关于损失残差与预处理更新几何之间闭环比对偏差的ISS型有界性结果。我们在多模态场景中评估了该方向可观测性感知的自适应学习方法，验证其在病理稀疏域和复杂任务中提升学习一致性和稳定性的有效性。
原文摘要 · Abstract (English)
What should a machine learning model learn when data is missing during training? We look at the learning process from a dynamical systems perspective, cast data missingness as a structured loss of actuation that limits controllability of the parameter error dynamics, and ultimately derive adaptation mechanisms with Lyapunov stability characteristics that throttle model updates in ways that preserve learning coherence under partial, intermittent observability. Under recurrent excitation, our analysis provides ISS-type residual-to-state bounds with respect to a bounded closed-loop mismatch between the loss residual and the preconditioned update geometry. We evaluate the efficacy of our directional observability-aware adaptive learning approach on multimodal contexts, reinforcing its premise in promoting learning coherence and stability even in pathologically sparse domains and problems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。