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.
CLARITree: Cholesky and Lookahead Accelerations for Regression with Interpretable Piecewise Linear Trees
Regression trees are among the most interpretable yet expressive model classes in machine learning. Historically, greedy induction has been the dominant approach for constructing well-performing regression trees.
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arXiv Machine Learning
Jul 31
Search Strategies for Optimal Classification and Regression Trees
arXiv:2607. 28170v1 Announce Type: new Abstract: Optimal decision trees (ODTs) are compact, interpretable machine learning models that globally optimize a given objective, but their scalability remains challenging.
arXiv AI
Jun 16
Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression
arXiv:2602. 22422v2 Announce Type: replace-cross Abstract: Smooth-basis models such as Chebyshev polynomial regressors and radial basis function (RBF) networks are well established in numerical analysis.
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
Jul 17
Selecting Hyperparameters for Tree-Boosting
arXiv:2602. 05786v3 Announce Type: replace Abstract: Tree-boosting is a widely used machine learning technique for tabular data.
arXiv Machine Learning
1d ago
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
Jul 30
TreeCCA: Canonical Correlation Analysis via Gradient-Boosted Trees
arXiv:2607. 27027v1 Announce Type: new Abstract: Gradient-boosted trees dominate tabular machine learning, yet canonical correlation analysis has always relied on linear or neural encoders.
arXiv Machine Learning
Jul 3
Conditional Inference Trees and Forests for Feature Selection
arXiv:2607. 01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive.
arXiv Machine Learning
Aug 5
Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling
arXiv:2608. 03111v1 Announce Type: new Abstract: Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width.
arXiv Machine Learning
Jun 19
A Solver-Free Training Method for Predict-then-Optimize
arXiv:2606. 19587v1 Announce Type: cross Abstract: We propose a scalable method for training prediction (machine learning) models in the predict-then-optimize paradigm, where model outputs serve as coefficients for a subsequent linear optimization task.
Hugging Face Trending Papers
Aug 4
Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling
Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width. For gradient boosting decision trees (GBDTs), however, an analogous single-axis capacity parameter has not been established.
arXiv Machine Learning
Jun 2
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
arXiv:2606. 02384v1 Announce Type: new Abstract: Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures.