**Learning Sparse Decision Trees via Transformer Variational Auto-Encoders** >

**Learning Sparse Decision Trees via Transformer Variational Auto-Encoders**

> Decision trees are among the most widely used models in machine learning, largely due to their transparent decision logic, making them well-suited for high-stakes decision-making contexts. However, most existing learning algorithms focus on predictive performance, overlooking the joint optimization of other desirable properties, such as structural sparsity. In this work we propose TREVIS, an approach for learning dec...

**来源信息**
- **来源**：HuggingFace Daily Papers（社区热门论文）
- **分类**：ai-models
- **发布时间**：2026-09-15 08:00（北京时间）
- **原文**：[打开原文](https://huggingface.co/papers/2609.01430)