一个无需调参的模型，能统一处理表格数据的密度估计与异常检测。
In-Context Density Estimation for Tabular Data
- 基于Transformer的上下文密度估计，读入数据即输出概率密度。
- 单次前向传播完成任务，无需训练、采样或超参数调整。
- 兼容四种任务，性能媲美专用模型，适合快速部署场景。
密度估计是表格数据中无监督任务（如异常检测、分布外检测、数据增强）的核心。传统方法需为每项任务单独建模并调参。本文提出ICED，一种基于能量的上下文密度估计器，通过一次预训练即可通用。该模型在合成先验数据上预训练，目标是在信息丰富区域拟合对数密度，同时保持其他区域的顺序一致性。推理时，仅需将数据作为上下文输入，即可在单次前向传播中返回任意查询点的未归一化对数密度，无需拟合、采样或超参数选择。一个冻结的ICED模型可驱动四种任务：密度估计、分布外检测、无监督异常检测和生成式数据增强，在所有任务中表现均接近最优专用方法，且无需重训练、调参或标签即可跨任务迁移。代码已开源。
原文摘要 · Abstract (English)
Density estimation underlies many unsupervised tasks on tabular data such as anomaly detection, out-of-distribution detection, and data augmentation. Although all these problems reduce to questions about where probability mass lies, they are typically solved individually by fitting a separate model to each dataset, with its own hyperparameters and tuning budget. We introduce ICED, an in-context, energy-based density estimator that removes this per-dataset cost. ICED is a transformer-based model pretrained once on a synthetic prior built specifically for density estimation under an objective that fits log-density where it is informative and preserves its ordering elsewhere. In the inference, it reads a dataset as context and returns an unnormalized log-density for any query point in a single forward pass, with no fitting, sampling, or hyperparameter selection. A single frozen ICED model then drives four tasks usually handled by four specialized pipelines: density estimation, out-of-distribution detection, unsupervised anomaly detection, and generative augmentation. Across all four, it is competitive with the strongest task-specific method, while being the only approach that needs no retraining, no tuning, and no labels to move between them. The code is available at https://github.com/gmum/iced.
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