arXiv Machine Learning
Jun 30
A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
Gradient boosting with vector-valued leafs
arXiv:2606. 29326v1 Announce Type: cross Abstract: Gradient boosting in the form of decision tree ensembles has successfully been applied to a variety of problems using simple objective functions based on log-likelihoods of a single variable.
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arXiv Machine Learning
Jul 16
Parallel gradient boosting for flexible estimation of conditional distributions
arXiv:2607. 13550v1 Announce Type: cross Abstract: Boosting is one of the most successful learning techniques for standard classification and regression tasks.
Hugging Face Trending Papers
Jul 15
Parallel gradient boosting for flexible estimation of conditional distributions
Boosting is one of the most successful learning techniques for standard classification and regression tasks. Its extension to multi-output prediction problems has found an increasing number of applications in recent years.
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 17
Analytical study of the optimal combination of binary classifiers based on classifiers-induced partitioning of the training set
arXiv:2607. 14889v1 Announce Type: new Abstract: This paper studies an optimal linear combination of binary classifiers based on a logical structuration of the dataset via truth tables.
arXiv Machine Learning
Jun 15
A General Framework for Decision Trees via Bregman Divergences
arXiv:2606. 13984v1 Announce Type: cross Abstract: Decision trees are one of the fundamental tools in statistical learning due to their interpretability, flexibility, and their ability to adapt to nonlinear structures.
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 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.
arXiv Machine Learning
Jun 5
On the Convergence of Multicalibration Gradient Boosting
arXiv:2602. 06773v2 Announce Type: replace Abstract: Multicalibration gradient boosting has recently emerged as a scalable method that empirically produces approximately multicalibrated predictors and has been deployed at web scale.
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
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 26
EMA-FS: Accelerating GBDT Training via Gain-Informed Feature Screening
arXiv:2606. 26337v1 Announce Type: new Abstract: Gradient Boosted Decision Trees (GBDT), exemplified by LightGBM, spend a dominant fraction of training time -- typically 65-70% -- constructing per-feature histograms.