Arman S. Zharmagambetov

Learning Interpretable, Tree-Based Projection Mappings for Nonlinear Embeddings

Model interpretability is a topic of renewed interest given today’s widespread practical use of machine learning, and the need to trust or understand automated predictions. We consider the problem of optimally learning interpretable out-of-sample mappings for nonlinear embedding methods such as $t$-…