NPBoost：具有梯度提升固定效应的神经过程
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
- ETH Zurich（苏黎世联邦理工学院）
- Lucerne University of Applied Sciences and Arts（卢塞恩应用科学与艺术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对神经过程在共享结构不规则时性能受限的问题，提出NPBoost，将响应分解为树提升固定效应与NP随机效应，联合训练提升元学习性能。
AI中文摘要：
神经过程（NPs）是基于模型的元学习器，它们隐式地学习一个随机过程，并从一个小型上下文集中适应新任务。大多数NPs的扩展侧重于改进神经网络架构。相反，我们开发了一种扩展，其动机来自元学习和混合效应模型的共享层次解释。具体来说，我们引入了神经过程提升（NPBoost），它将结构化响应变异性分解为跨任务共享的树提升固定效应和捕获随机任务间变异的NP随机效应。我们提出使用一种提升算法联合训练这两个组件，其中NP学习残差任务特定结构，而树集成估计跨任务的共同模式。在合成和真实世界的表格元学习问题中，当共享结构包含提升树能够有效表示的不连续性或其他不规则模式时，这种分解优于标准NP。
英文摘要：
Neural Processes (NPs) are model-based meta-learners that implicitly learn a stochastic process and adapt to a new task from a small context set. Most extensions of NPs focus on improving the neural network architecture. We instead develop an extension motivated by the shared hierarchical interpretation of meta-learning and mixed-effects models. Specifically, we introduce Neural Process Boosting (NPBoost), which decomposes structured response variability into tree-boosted fixed effects shared across tasks and NP random effects that capture stochastic task-to-task variation. We propose to train the two components jointly using a boosting algorithm in which an NP learns residual task-specific structure and a tree ensemble estimates common patterns across tasks. Across synthetic and real-world tabular meta-learning problems, this decomposition improves over a standard NP when the shared structure contains discontinuities or other irregular patterns that boosted trees can represent effectively.
