Problem
Research questions and friction points this paper is trying to address.
Neural Processes
meta-learning
mixed-effects models
task-to-task variation
discontinuities
NPBoost: Neural Processes with Gradient-Boosted Fixed Effects
🤖 AI Summary
研究提出NPBoost，通过结合树增强固定效应和神经过程随机效应，改进了神经过程模型在处理跨任务共享的不连续或非规则模式时的表现。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neural Processes
Gradient-Boosted Fixed Effects
Meta-learning
Mixed-effects Models
Tree-boosted
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