This paper extends gradient boosting to functions of vector inputs using a simple algorithm with histogram-based decision trees.

The paper introduces a new anytime-valid inference method to correct split selection in online decision trees, providing robust statistical guarantees for streaming data that existing methods lack.

The twoblock clustering tree is introduced as a new type of interpretable regression tree for multivariate responses, using local multivariate linear models as leaves and twoblock dimension reduction…

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

The paper proposes $\sf PVODTE$, a novel two-server protocol for private and verifiable outsourcing of decision tree evaluation that eliminates server-to-server communication, making it suitable for W…

The paper introduces TrEEStealer, a novel side-channel attack that efficiently steals Decision Trees (DTs) protected within Trusted Execution Environments (TEEs), demonstrating that TEEs fail to provi…

This paper proposes using genetic programming (GP) to jointly evolve both the feature sets and the structure of survival trees, resulting in highly interpretable and high-performing shallow models for…

The paper proposes a structural method using decision tree rulesets and multiple complementary metrics to detect concept drift in evolving malware families, finding that fixed-interval windowing with…

This paper proposes a lightweight, machine learning-based model for on-device intrusion detection in resource-constrained IoT devices, achieving high detection accuracy for common cyber threats.

The paper proposes a robust, multi-stage pipeline combining rule-based classification and machine learning to map noisy retail product names to standardized consumption categories, finding that simple…

This paper introduces a fuzzy-logic-based qualitative extension of Answer Set Programming (ASP) to integrate numerical information and qualitative reasoning.

This paper introduces a method to automatically determine the optimal learning period ($ au$) for the Random Gradient hyper-heuristic, enabling it to optimally solve Pseudo-Boolean Problems without ma…

This paper introduces ZipLine, a system for integrative analysis of multivariate graphs through a unified predicate language and learning algorithm.

This paper compares traditional machine learning models (Random Forests, XGBoost, SVM) against a complex Unified Multi-Task Time Series Model for churn prediction, concluding that conventional methods…

This paper presents an optimized algorithm for computing cut sets of a path set in graph theory and introduces a vectorized computational framework for property calculations.

The paper proposes 2FFS, a two-fidelity tree-search algorithm that efficiently identifies the best action in stochastic minimax trees by adaptively combining cheap, biased heuristic evaluations with e…