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David Fleischer

Doctorat - McGill
Superviseur⋅e principal⋅e
Archer Yang
Sujets de recherche
Apprentissage profond
Causalité
Théorie de l'apprentissage automatique
GitHub

Publications

Generalized Random Forests Using Fixed-Point Trees
David Fleischer
David A. Stephens
Archer Y. Yang
We propose a computationally efficient alternative to generalized random forests (GRFs) for estimating heterogeneous effects in large dimens… (voir plus)ions. While GRFs rely on a gradient-based splitting criterion, which in large dimensions is computationally expensive and unstable, our method introduces a fixed-point approximation that eliminates the need for Jacobian estimation. This gradient-free approach preserves GRF’s theoretical guarantees of consistency and asymptotic normality while significantly improving computational efficiency. We demonstrate that our method achieves a speedup of multiple times over standard GRFs without compromising statistical accuracy. Experiments on both simulated and real-world data validate our approach. Our findings suggest that the proposed method is a scalable alternative for localized effect estimation in machine learning and causal inference applications.
2025-10-06
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press