Kuat Gazizov presents faster regression-tree training at NeurIPS 2025

Kuat Gazizov, a PhD student at the University of California, Merced, presented “A Faster Training Algorithm for Regression Trees with Linear Leaves, and an Analysis of Its Complexity”. Kuat shows that Tree Alternation Optimization for regression trees can be accelerated substantially with the Sherman–Morrison–Woodbury formula while preserving accuracy. This lets deep trees train faster and potentially even outperform ordinary linear regression in training speed.

Congratulations to Kuat on a successful publication, and we wish him continued success in research!