引入拓扑感知与LLM的自动化特征工程新方法，提升特征质量与模型性能。
arXiv:2607.23286v1 Announce Type: cross Abstract: Automatic feature engineering (AutoFE) for tabular learning can be naturally formulated as a program…
将数值和嵌入特征转化为令牌，突破LLM推荐系统的性能瓶颈。
arXiv:2607.10016v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as backbone architectures for recommender systems…
实时AI核心技巧：滚动聚合如何高效处理时间窗口特征，一文讲透原理与实战
Article URL: https://www.hopsworks.ai/post/rolling-aggregations-for-real-time-ai Comments URL: https://news.ycombinator.com/item?id=48659199 Points: 1…
揭秘大模型越狱漏洞的底层机制，从概念对齐到脆弱特征的因果定位。
arXiv:2604.23130v2 Announce Type: replace Abstract: Jailbreak attacks expose a persistent failure mode in safety-aligned LLMs: models can be pushed in…
通过LLM诱导实现零样本主动特征获取，为机器学习中的数据稀疏问题提供新思路。
arXiv:2606.18933v1 Announce Type: new Abstract: Active feature acquisition (AFA) sequentially selects which features to observe to reach a classificat…
LLM自动化特征工程新方法MedFeat，模型感知与可解释性驱动，提升临床表格预测性能。
arXiv:2603.02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering o…
首个系统评估LLM在表格特征工程中表现的框架，帮你理解大模型如何自动生成有效特征。
arXiv:2606.09004v1 Announce Type: new Abstract: Feature engineering remains essential for tabular data analysis, and Large Language Models (LLMs) have…
将特征工程定义为智能体代码生成问题，颠覆传统数据变换范式，直击企业AI云资源预测的痛点。
arXiv:2605.25297v1 Announce Type: cross Abstract: Effective features are crucial for predictive model performance, but creating them often requires do…
大模型驱动自动特征工程，协作贝叶斯超参数优化提效，KDD 2026前沿方法。
arXiv:2602.09851v2 Announce Type: replace Abstract: Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottlenec…