AI RESEARCH
Learning Tree-Based Models with Gradient Descent
arXiv CS.LG
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ArXi:2603.11117v1 Announce Type: new Tree-based models are widely recognized for their interpretability and have proven effective in various application domains, particularly in high-stakes domains. However, learning decision trees (DTs) poses a significant challenge due to their combinatorial complexity and discrete, non-differentiable nature. As a result, traditional methods such as CART, which rely on greedy search procedures, remain the most widely used approaches. These methods make locally optimal decisions at each node, cons