variational autoencoders

Learning Sparse Decision Trees via Transformer Variational Auto-Encodersarxiv.org

The paper presents TREVIS, a method that encodes decision trees into a continuous latent space using a Tree Transformer VAE, allowing gradient-based optimization of both accuracy and structural sparsity. Experiments claim TREVIS matches the predictive performance of near-optimal algorithms while producing sparser trees.