通过单调交替样条实现高效时间点过程
Efficient Temporal Point Processes via Monotone Alternating Splines
AI总结：
针对单调神经网络在建模累积条件强度函数时的结构缺陷，提出单调交替样条框架，通过分离插值与外推组件提升表示能力与计算效率，在合成和真实数据集上取得更优性能。
摘要：
时间点过程（TPPs）在多个领域有广泛应用。与建模TPP的条件强度相比，建模其累积条件强度函数（CCIF）可提高计算效率并消除数值近似误差。然而，当前的CCIF参数化统一依赖于单调神经网络（MNNs），我们发现其存在三个结构性瓶颈——凸性限制、饱和极限以及违反CCIF建模要求——从根本上限制了其对复杂时间动态的表示能力。为解决这些瓶颈，本文提出了一种称为单调交替样条（MAS）的新框架。通过利用不同的插值和外推组件，MAS为建模CCIF提供了灵活高效的框架。理论上，MAS的插值提供了强拟合精度，而其外推支持鲁棒泛化，减少了MNNs的不可约逼近误差。大量实验表明，MAS在合成和真实数据集上均取得了优越性能。
英文摘要：
Temporal point processes (TPPs) have widespread applications across various domains. Compared to modeling the conditional intensity of a TPP, modeling its cumulative conditional intensity function (CCIF) improves computational efficiency and eliminates numerical approximation errors. However, current CCIF parameterizations uniformly rely on Monotone Neural Networks (MNNs), which we identify as suffering from three structural deadlocks--convexity restrictions, saturation limits, and violations of CCIF modeling requirements--that fundamentally restrict their representational capacity for complex temporal dynamics. To resolve these bottlenecks, this paper proposes a novel framework called Monotone Alternating Splines (MAS). By leveraging distinct interpolation and extrapolation components, MAS provides a flexible and efficient framework for modeling CCIFs. Theoretically, MAS's interpolation provides strong fitting accuracy, while its extrapolation supports robust generalization, reducing the irreducible approximation gaps of MNNs. Extensive experiments show that MAS achieves superior performance on both synthetic and real-world datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
