Alessio Cascione

TREVIS is proposed, 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), that replaces the discrete search space with a continuous one, enabling gradient-based optimization via a differentiable surrogate model.