Tootfinder

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Brian Liu, Rahul Mazumder, Peter Radchenko
https://arxiv.org/abs/2506.20114

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key novelty of our estimator…

TRUST: Transparent, Robust and Ultra-Sparse Trees
Albert Dorador
https://arxiv.org/abs/2506.15791 https://arxiv.org/pdf/2506.15791

TRUST: Transparent, Robust and Ultra-Sparse Trees
Piecewise-constant regression trees remain popular for their interpretability, yet often lag behind black-box models like Random Forest in predictive accuracy. In this work, we introduce TRUST (Transparent, Robust, and Ultra-Sparse Trees), a novel regression tree model that combines the accuracy of Random Forests with the interpretability of shallow decision trees and sparse linear models. TRUST further enhances transparency by leveraging Large Language Models to generate tailored, user-friendl…

Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
Toon Van Puyvelde, Mehran Zareh, Chris Develder
https://arxiv.org/abs/2506.01641

Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
In recent years, deep reinforcement learning (DRL) algorithms have gained traction in home energy management systems. However, their adoption by energy management companies remains limited due to the black-box nature of DRL, which fails to provide transparent decision-making feedback. To address this, explainable reinforcement learning (XRL) techniques have emerged, aiming to make DRL decisions more transparent. Among these, soft differential decision tree (DDT) distillation provides a promisin…

This https://arxiv.org/abs/2501.15056 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Feedback-Aware Monte Carlo Tree Search for Efficient Information Seeking in Goal-Oriented Conversations
Effective decision-making and problem-solving in conversational systems require the ability to identify and acquire missing information through targeted questioning. A key challenge lies in efficiently narrowing down a large space of possible outcomes by posing questions that minimize uncertainty. To address this, we introduce a novel framework that leverages Large Language Models (LLMs) to generate information-seeking questions, with Monte Carlo Tree Search (MCTS) to strategically select quest…

This https://arxiv.org/abs/2412.06552 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCC_…

Direct Sums for Parity Decision Trees
Direct sum theorems state that the cost of solving $k$ instances of a problem is at least $Ω(k)$ times the cost of solving a single instance. We prove the first such results in the randomised parity decision tree model. We show that a direct sum theorem holds whenever (1) the lower bound for parity decision trees is proved using the discrepancy method; or (2) the lower bound is proved relative to a product distribution.

The ATLAS Virtual Research Assistant
H. F. Stevance, K. W. Smith, S. J. Smartt, S. J. Roberts, N. Erasmus, D. R. Young, A. Clocchiatti
https://arxiv.org/abs/2506.09778

The ATLAS Virtual Research Assistant
We present the Virtual Research Assistant (VRA) of the ATLAS sky survey which performs preliminary eyeballing on our clean transient data stream. The VRA uses Histogram Based Gradient Boosted Decision Tree Classifiers trained on real data to score incoming alerts on two axes: "Real" and "Galactic". The alerts are then ranked using a geometric distance such that the most "Real" and "Extra-galactic" receive high scores; the scores are updated when new light curve data is obtained on subsequent vi…

This https://arxiv.org/abs/2506.01220 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Vulnerability Management Chaining: An Integrated Framework for Efficient Cybersecurity Risk Prioritization
Cybersecurity teams face an overwhelming vulnerability crisis: with 25,000+ new CVEs disclosed annually, traditional CVSS-based prioritization requires addressing 60% of all vulnerabilities while correctly identifying only 20% of those actually exploited. We propose Vulnerability Management Chaining, an integrated decision tree framework combining historical exploitation evidence (KEV), predictive threat modeling (EPSS), and technical impact assessment (CVSS) to transform vulnerability manageme…

This https://arxiv.org/abs/2503.12181 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Value Gradients with Action Adaptive Search Trees in Continuous (PO)MDPs
Solving Partially Observable Markov Decision Processes (POMDPs) in continuous state, action and observation spaces is key for autonomous planning in many real-world mobility and robotics applications. Current approaches are mostly sample based, and cannot hope to reach near-optimal solutions in reasonable time. We propose two complementary theoretical contributions. First, we formulate a novel Multiple Importance Sampling (MIS) tree for value estimation, that allows to share value information b…