12 DAILY CVPR Saturday Congrats, Doctor Sascha! Let’s face it — decision trees (DTs) were probably one of the first model you ever used in a machine learning course. They’re intuitive, interpretable, and often overlooked the moment we move on to deep learning. I chose a different route: instead of leaving DTs behind, I doubled down on them. During my PhD, I developed a method that allows hard, axis-aligned DTs to be trained using gradient descent — just like neural networks. This turns traditional tree induction on its head: no more greedy, locally optimal splitby-split procedures (Figure 1). Instead, all parameters — including thresholds, features, and even leaf outputs — are optimized jointly using backpropagation. Sounds simple? It’s not. Hard, axis-aligned splits are non-differentiable by nature. But by combining a dense, parameter-based tree representation (Figure 2) with the straight-through operator and soft approximations during backpropagation, we can calculate meaningful gradients — all while keeping the tree structure hard and interpretable throughout training. The Sascha Marton recently defended his PhD at the University of Mannheim and is now Assistant Professor at the Technical University of Clausthal. At the CORE lab, Sascha continues to explore structured and interpretable learning systems. His research brings decision trees — one of the most classic machine learning models — back to the spotlight with a fresh twist: teaching them to learn like neural networks. The resulting gradient-based trees strike a balance between transparency and performance, showing promising results across tabular learning, multimodal architectures, and even reinforcement learning. From Roots to Gradients: Teaching Trees to Learn Differently
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