用预训练表格模型实时处理数据流，效果优于传统树集成方法。
In-context Learning of Evolving Data Streams with Tabular Foundational Models
- 用提示调优实现上下文学习，通过滑动记忆摘要持续数据流。
- 在非平稳数据流上，性能超越自适应随机森林和流式随机斑块。
- 适合需要快速适应变化数据的工业实时预测场景。
当前最先进的数据流挖掘长期依赖于非常快速决策树（Very Fast Decision Tree）的集成，该算法曾获2015年KDD时间检验奖。然而，大型表格模型（即专为结构化数值数据设计的Transformer）的出现标志着重大范式转变。这些模型不再依赖传统权重更新，而是通过提示调优实现上下文学习。通过实时生成的数据流草图摘要无界数据，可将其输入预训练模型进行高效处理。本研究融合两领域进展，指出Transformer的隐式元学习能力、在漂移自然数据上的预训练，以及对上下文优化的依赖，能直接应对动态环境中的自适应学习挑战。实验表明，将TabPFN与简单滑动记忆策略结合，在所有非平稳基准测试中持续优于自适应随机森林和流式随机斑块等树集成方法。
原文摘要 · Abstract (English)
State-of-the-art data stream mining has long drawn from ensembles of the Very Fast Decision Tree, a seminal algorithm honored with the 2015 KDD Test-of-Time Award. However, the emergence of large tabular models, i.e., transformers designed for structured numerical data, marks a significant paradigm shift. These models move beyond traditional weight updates, instead employing in-context learning through prompt tuning. By using on-the-fly sketches to summarize unbounded streaming data, one can feed this information into a pre-trained model for efficient processing. This work bridges advancements from both areas, highlighting how transformers' implicit meta-learning abilities, pre-training on drifting natural data, and reliance on context optimization directly address the core challenges of adaptive learning in dynamic environments. Exploring real-time model adaptation, this research demonstrates that TabPFN, coupled with a simple sliding memory strategy, consistently outperforms ensembles of Hoeffding trees, such as Adaptive Random Forest, and Streaming Random Patches, across all non-stationary benchmarks.
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