arXiv AI
Jun 2
From Rashomon Theory to PRAXIS: Efficient Decision Tree Rashomon Sets
arXiv:2606. 00202v1 Announce Type: cross Abstract: Standard machine learning pipelines often admit many near-optimal models.
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.
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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 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 AI
Jul 3
Algebraic Model Counting for Global Analysis of Optimal Decision Trees
arXiv:2607. 02069v1 Announce Type: new Abstract: Ensuring model reliability in Explainable AI requires a global assessment of the hypothesis space.
arXiv Machine Learning
Sep 10
Sparse Oblique Rule Boosting for Simpler Additive Rule Ensembles
arXiv:2609.06426v1 Announce Type: new Abstract: Small additive ensembles of symbolic rules offer interpretable prediction models. Traditionally, these ensembles use rule conditions based on conjuncti...
arXiv AI
3d ago
A Moving-Horizon Approximate Branch-and-Reduce Method for Deep Classification Trees
The paper introduces a moving-horizon approximate branch‑and‑reduce method for training deep classification trees on large datasets with continuous features. It combines a hierarchical root‑subtree optimization framework, branch‑and‑reduce at the root, greedy heuristics for subtrees, and a low‑cost moving‑horizon refinement to improve accuracy. Experiments show the approach surpasses heuristic baselines in test accuracy while scaling better in dataset size and tree depth than existing global optimal solvers.
arXiv Machine Learning
Aug 18
Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles
arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.
arXiv Machine Learning
Aug 6
Data-Aware and Scalable Sensitivity Analysis for Decision Tree Ensembles
arXiv:2602. 07453v2 Announce Type: replace Abstract: Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness.
Hugging Face Trending Papers
Jul 2
Algebraic Model Counting for Global Analysis of Optimal Decision Trees
Ensuring model reliability in Explainable AI requires a global assessment of the hypothesis space. We propose a formal framework for the exhaustive analysis of optimal and near-optimal decision trees, called Algebraic Decision Tree Counting (ADTC).
arXiv AI
Jul 7
Exploring the Rashomon Set for Concept-Based Models
arXiv:2511. 19636v2 Announce Type: replace-cross Abstract: In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on fundamentally different internal logic.
arXiv AI
Sep 24
Parameter-Efficient Construction of the Rashomon Slice for Concept Bottleneck Models
The paper introduces a method for efficiently exploring the Rashomon set of Concept Bottleneck Models (CBMs) by using a parallel parameter‑efficient adaptation module, checkpointing, and a concept diversity objective. This approach generates multiple equally accurate CBMs from a single training process, achieving greater diversity than baseline methods while consuming less memory. The resulting diverse models enable trustworthy selection, reduce inter‑class confusion, and support reliable abstention in decision‑making.
arXiv Machine Learning
Jun 5
Quantifying Sensitivity for Tree Ensembles: A symbolic and compositional approach
arXiv:2605. 13830v2 Announce Type: replace-cross Abstract: Decision tree ensembles (DTE) are a popular model for a wide range of AI classification tasks, used in multiple safety critical domains, and hence verifying properties on these models has been an active topic of study over the last decade.