arXiv Machine Learning
Partially Interpretable Decision Trees
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 1
Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again
arXiv:2606. 30995v1 Announce Type: new Abstract: Recent work has shown that well-optimized individual decision trees can match complex black box models in some settings, primarily in noisy domains.
arXiv Machine Learning
Jun 24
Experiments with Optimal Model Trees
arXiv:2503. 12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems.
arXiv AI
Oct 9
Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations
The paper presents a scalable method for meta‑learning interpretable decision trees by generating synthetic pre‑training data. It samples near‑optimal trees to create large, realistic datasets and uses the MetaTree transformer architecture to match the performance of real‑world pre‑training or costly optimal tree training. This approach cuts computational costs, offers flexible data generation, and supports efficient, scalable meta‑learning of decision trees.
arXiv Machine Learning
Aug 11
Backward Compatibility in Tree-Based Explanations and Enhanced CART Algorithm
arXiv:2608. 08674v1 Announce Type: new Abstract: In the operation of machine learning models, model update is a fundamental process that requires careful consideration of its impact on downstream decision-making.
arXiv AI
Oct 27
The Hitchhiker's Guide to System 1 Models: Fast Decision-Making Beyond LLMS
The paper introduces System 1 models—fast, single‑pass decision engines that map inputs directly to structured outputs, addressing the latency, cost, and calibration issues of large language models. It presents a five‑family taxonomy (encoder‑based classifiers, reward models, non‑autoregressive engines, contrastive models, and extraction models) and discusses training, deployment trade‑offs, and calibration theory, including proper scoring rules and RLCD. The guide also covers integration into agentic workflows, System 1+System 2 cascades, 18 application domains, an open‑source ecosystem, and practical resources such as code, a model zoo, and assessment questions.
arXiv Machine Learning
Aug 6
ArborEnum: Decision Tree Rashomon Sets over Continuous Features
arXiv:2608. 04310v1 Announce Type: new Abstract: The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant ramifications for robustness, feature importance, and customizability.