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 decision trees with respect to complex objectives, based on the exploration of the latent space of a Tree Transformer Variational Auto-Encoder (TTVAE). By mapping decision trees onto
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- LinkedLinked via arxiv author · 85%Giacomo Fidone →
“Learning Sparse Decision Trees via Transformer Variational Auto-Encoders”
- LinkedLinked via arxiv author · 85%Alessio Cascione →
“Learning Sparse Decision Trees via Transformer Variational Auto-Encoders”
- LinkedLinked via arxiv author · 85%Riccardo Guidotti →
“Learning Sparse Decision Trees via Transformer Variational Auto-Encoders”
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “Learning Sparse Decision Trees via Transformer Variational A” ≈ “amitness/learning””