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.
Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance
arXiv:2409. 12788v3 Announce Type: replace Abstract: Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric.
Related stories
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 3
How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
arXiv:2606. 03549v1 Announce Type: new Abstract: Hyperparameter optimization (HPO) for Random Forest faces a specific difficulty in tuning the number of trees: the predictive score typically improves monotonically with ensemble size, so standard methods such as Tree-structured Parzen Estimator (TPE) and Hyperband require a predefined search range and often drive the estimate toward its right boundary.
Hugging Face Trending Papers
Jun 2
How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration
Hyperparameter optimization (HPO) for Random Forest faces a specific difficulty in tuning the number of trees: the predictive score typically improves monotonically with ensemble size, so standard methods such as Tree-structured Parzen Estimator (TPE) and Hyperband require a predefined search range and often drive the estimate toward its right boundary. Early-stopping strategies avoid fixing such a range, but can be sensitive to score noise and prone to premature stopping.
arXiv Machine Learning
Aug 12
Simpler Logarithmic Approximation Algorithms for the Optimal Decision Tree and Adaptive Set Cover
arXiv:2604. 12036v3 Announce Type: replace-cross Abstract: We study a well-known task of constructing a decision tree identifying an unknown hypothesis from a given ground set of hypotheses under both the average- and worst-case cost.
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 14
Precedence-Constrained Decision Trees and Coverings
arXiv:2602. 21312v4 Announce Type: replace-cross Abstract: This work considers a number of optimization problems and reductive relations between them.
arXiv Machine Learning
Jun 4
Ternary Decision Trees with Locally-Adaptive Uncertainty Zones
arXiv:2605. 22740v2 Announce Type: replace Abstract: Decision trees assign identical confidence to instances near and far from each split threshold.
arXiv Machine Learning
Jun 17
Amortizing Maximum Inner Product Search with Learned Support Functions
arXiv:2603. 08001v2 Announce Type: replace Abstract: Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (the keys) that best aligns with a given query.
arXiv AI
Jul 21
Normalized Rewards for Preference Optimization
arXiv:2607. 16240v1 Announce Type: cross Abstract: Direct Alignment Algorithms (DAAs) such as DPO have become a common way to post-train and align LLMs with human preferences.
arXiv AI
Jun 11
TreeSeeker: Tree-Structured Trial, Error, and Return in Deep Search
arXiv:2606. 11662v1 Announce Type: new Abstract: Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis.
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.