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A new moving-horizon approximate branch-and-reduce method for training near-optimal deep classification trees on large-scale datasets with continuous features, achieving better accuracy than heuristic baselines and far greater scalability than global optimal solvers.
The paper proposes TREVIS, a method that uses a Tree Transformer Variational Auto-Encoder to learn sparse decision trees by optimizing in a continuous latent space, achieving good predictive performance with improved structural sparsity.
This paper introduces ArborEnum, the first algorithm to exactly enumerate decision-tree Rashomon sets over continuous features without binarization, along with relaxed and anytime approximations that achieve orders-of-magnitude speedups while preserving near-perfect recall.
This paper proposes a hierarchical, interpretable-by-design pivot selection model based on proximity trees and ensemble learning. It is data-modality-agnostic and demonstrates competitive results across tabular, text, image, and time-series datasets.
Introduces Multistage Defer Trees, a sequence of sparse decision trees that defer hard samples to later trees or a black box, aiming to match ensemble accuracy while keeping most predictions interpretable.
PRAXIS is a new algorithm that efficiently approximates the Rashomon set of near-optimal decision trees, achieving orders of magnitude improvement in runtime and memory while maintaining near-perfect recall.
The author argues that deterministic decision trees will always outperform neural networks, claiming that AI's successes are only due to computational limits on building such trees.
This paper investigates disagreement-based drift detection in ensembles of incremental decision trees, finding that while effective in neural networks, the method underperforms loss-based detectors for tree ensembles due to limited model plasticity.