:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Hinterleitner, Alexander, Bartz-Beielstein, Thomas
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	https://arxiv.org/abs/2505.07910
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Simplifying Hyperparameter Tuning in Online Machine Learning -- The spotRiverGUI
di: Bartz-Beielstein, Thomas
Pubblicazione: (2024)

Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution
di: Hinterleitner, Alexander, et al.
Pubblicazione: (2024)

Multi-Task Optimization over Networks of Tasks
di: Hatzky, Julian, et al.
Pubblicazione: (2026)

Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
di: Bartz-Beielstein, Thomas
Pubblicazione: (2025)

Bed-Attached Vibration Sensor System: A Machine Learning Approach for Fall Detection in Nursing Homes
di: Bartz-Beielstein, Thomas, et al.
Pubblicazione: (2024)

A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity
di: Chandrasekaran, Sowmya, et al.
Pubblicazione: (2024)

Optimization with SpotOptim
di: Bartz-Beielstein, Thomas
Pubblicazione: (2026)

Why we need an AI-resilient society
di: Bartz-Beielstein, Thomas
Pubblicazione: (2019)

Faithful and Stable Neuron Explanations for Trustworthy Mechanistic Interpretability
di: Yan, Ge, et al.
Pubblicazione: (2025)

Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data
di: Kobylińska, Katarzyna, et al.
Pubblicazione: (2023)

Trustworthy Graph Neural Networks: Aspects, Methods and Trends
di: Zhang, He, et al.
Pubblicazione: (2022)

On Trustworthy Rule-Based Models and Explanations
di: Siala, Mohamed, et al.
Pubblicazione: (2025)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
di: Zhang, Jiaxing, et al.
Pubblicazione: (2025)

On Tuning Neural ODE for Stability, Consistency and Faster Convergence
di: Akhtar, Sheikh Waqas
Pubblicazione: (2023)

Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning
di: Chen, Yanda, et al.
Pubblicazione: (2024)

Pathwise Explanation of ReLU Neural Networks
di: Lim, Seongwoo, et al.
Pubblicazione: (2025)

Time-Series Forecasting in Safety-Critical Environments: An EU-AI-Act-Compliant Open-Source Package / Zeitreihenprognose in sicherheitskritischen Umgebungen: Ein KI-VO-konformes Open-Source-Paket
di: Bartz-Beielstein, Thomas, et al.
Pubblicazione: (2026)

The GECo algorithm for Graph Neural Networks Explanation
di: Calderaro, Salvatore, et al.
Pubblicazione: (2024)

Regularizing Explanations in Bayesian Convolutional Neural Networks
di: Bekkemoen, Yanzhe, et al.
Pubblicazione: (2021)

Bayesian Optimization for Hyperparameters Tuning in Neural Networks
di: Onorato, Gabriele
Pubblicazione: (2024)

Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training
di: Wang, Ting, et al.
Pubblicazione: (2025)

Graph Neural Network Causal Explanation via Neural Causal Models
di: Behnam, Arman, et al.
Pubblicazione: (2024)

Uncertainty Quantification for Gradient-based Explanations in Neural Networks
di: Mulye, Mihir, et al.
Pubblicazione: (2024)

Game-theoretic Counterfactual Explanation for Graph Neural Networks
di: Chhablani, Chirag, et al.
Pubblicazione: (2024)

FAME: Formal Abstract Minimal Explanation for Neural Networks
di: Boumazouza, Ryma, et al.
Pubblicazione: (2026)

How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
di: Ma, Bin, et al.
Pubblicazione: (2025)

Multi-Objective Optimization with Desirability and Morris-Mitchell Criterion
di: Bartz-Beielstein, Thomas, et al.
Pubblicazione: (2025)

Counterfactual Explanations for Hypergraph Neural Networks
di: Veglianti, Fabiano, et al.
Pubblicazione: (2026)

LIMEtree: Consistent and Faithful Surrogate Explanations of Multiple Classes
di: Sokol, Kacper, et al.
Pubblicazione: (2020)

Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
di: Binkyte, Ruta, et al.
Pubblicazione: (2025)

Obtaining Example-Based Explanations from Deep Neural Networks
di: Dong, Genghua, et al.
Pubblicazione: (2025)

Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
di: Deng, Huiqi, et al.
Pubblicazione: (2025)

A Model of Causal Explanation on Neural Networks for Tabular Data
di: Isozaki, Takashi, et al.
Pubblicazione: (2025)

On the Consistency of GNN Explanations for Malware Detection
di: Shokouhinejad, Hossein, et al.
Pubblicazione: (2025)

LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
di: Chang, Chih-Yu, et al.
Pubblicazione: (2025)

GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks
di: Li, Xuyan, et al.
Pubblicazione: (2025)

Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations
di: Palacio, David N., et al.
Pubblicazione: (2024)

Edge Prompt Tuning for Graph Neural Networks
di: Fu, Xingbo, et al.
Pubblicazione: (2025)

Universal Prompt Tuning for Graph Neural Networks
di: Fang, Taoran, et al.
Pubblicazione: (2022)

Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
di: Jiang, Junqi, et al.
Pubblicazione: (2023)