Decision Tree
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
Computer Science > Machine Learning [Submitted on 1 May 2026 (v1), last revised 21 May 2026 (this version, v2)] Title:Trees to Flows and Back: Unifying Decision Trees and Diffusion Models View PDFAbstract:Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work unifies the two by establishing a crisp mathematical correspondence between hierarchical decision trees and diffusion processes in...
Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors
Computer Science > Machine Learning [Submitted on 31 May 2026] Title:Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors View PDF HTML (experimental)Abstract:We ask: when do Bayesian model averaging (BMA) weights over decision trees carry sufficient epistemic information to justify committed exploitation of the averaging distribution?
Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors
Computer Science > Machine Learning [Submitted on 31 May 2026 (v1), last revised 2 Jun 2026 (this version, v2)] Title:Sample Complexity and Decision-Theoretic Guarantees for Bayesian Model Averaging over Decision Trees with Catalan-Exponential Priors View PDF HTML (experimental)Abstract:We ask: when do Bayesian model averaging (BMA) weights over decision trees carry sufficient epistemic information to justify committed exploitation of the averaging distribution?
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. We introduce ternary decision trees, which augment each split node with an uncertainty zone of half-width delta. A decision-theoretic framework characterises the optimal zone width delta* as the solution to a node-local cost-minimisation problem; four formal properties are established: accuracy decomposition, a sufficiency condition for decided...
Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference
arXiv:2605.31239v1 Announce Type: cross Abstract: Bagging-based ensembles, most notably Adaptive Random Forests, are among the strongest performers for learning from data streams. A common denominator across these methods is their reliance on Hoeffding Trees as base learners, which grow decision trees incrementally by testing whether a candidate split is significantly better than its alternatives using concentration inequalities. Despite their empirical success, existing variants lack valid...
Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification
arXiv:2605.20716v5 Announce Type: replace Abstract: Random forests construct each tree with a different, randomised representation of the feature space. Their uniform voting cannot correct errors in regions where trees with incorrect representations probabilistically outnumber correct ones, even when the ensemble collectively holds enough correct information - a reducible error that this paper addresses. We propose using the structural pattern of each tree's decision path as an...
A 65 nm Trustworthy Hypoglycemia Forecasting Engine Achieving 11.3 nJ per Inference
arXiv:2606.07455v1 Announce Type: new Abstract: Diabetes affects millions of people and requires reliable continuous glucose monitoring for early hypoglycemia warning. However, medical AI systems must be not only accurate and energy efficient, but also explainable, noise robust, and uncertainty aware. This work presents a 65 nm hypoglycemia forecasting engine based on probabilistic decision trees for trustworthy medical inference.
Tree Containment Parameterized by Scanwidth
Announce Type: new Abstract: TREE CONTAINMENT is a central decision problem in mathematical phylogenetics, asking whether a given rooted phylogenetic tree is embeddable in ("displayed by") a given rooted phylogenetic network. While the problem is NP-complete for general networks, many algorithmic advances have relied on structural parameters that capture how "tree-like" a network is. In this paper we investigate TREE CONTAINMENT under the structural parameter scanwidth, a directed width...
Tree Containment Parameterized by Scanwidth
arXiv:2605.31071v2 Announce Type: replace Abstract: TREE CONTAINMENT is a central decision problem in mathematical phylogenetics, asking whether a given rooted phylogenetic tree is embeddable in ("displayed by") a given rooted phylogenetic network. While the problem is NP-complete for general networks, many algorithmic advances have relied on structural parameters that capture how "tree-like" a network is. In this paper we investigate TREE CONTAINMENT under the structural parameter...
Explainably Safe Reinforcement Learning
Announce Type: new Abstract: Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior. This is particularly important for learned systems, whose decision-making processes are often highly opaque. Shielding is a prominent model-based technique for enforcing safety in reinforcement learning.