Publications [#372436] of Cynthia D. Rudin

Papers Published

1. Zhang, R; Xin, R; Seltzer, M; Rudin, C, Optimal Sparse Regression Trees, Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, vol. 37 (June, 2023), pp. 11270-11279, ISBN 9781577358800 [doi]
   (last updated on 2025/01/27)

   Abstract:
   Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications. Within the large literature on regression trees, there has been little effort towards full provable optimization, mainly due to the computational hardness of the problem. This work proposes a dynamic-programming-with-bounds approach to the construction of provably-optimal sparse regression trees. We leverage a novel lower bound based on an optimal solution to the k-Means clustering algorithm on one dimensional data. We are often able to find optimal sparse trees in seconds, even for challenging datasets that involve large numbers of samples and highly-correlated features.