Imagen de Portada

Explainability of Machine Learning Models under Missing Data

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Vo, Tuan L., Nguyen, Thu, Lopez-Ramos, Luis M., Hammer, Hugo L., Riegler, Michael A., Halvorsen, Pal
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Imputation using training labels and classification via label imputation
por: Nguyen, Thu, et al.
Publicado: (2023)

Principal Components for Neural Network Initialization
por: Phan, Nhan, et al.
Publicado: (2025)

Probabilistic Runtime Verification, Evaluation and Risk Assessment of Visual Deep Learning Systems
por: Torpmann-Hagen, Birk, et al.
Publicado: (2025)

DPERC: Direct Parameter Estimation for Mixed Data
por: Vo, Tuan L., et al.
Publicado: (2025)

Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach
por: Boeker, Matthias, et al.
Publicado: (2024)

Directly Handling Missing Data in Linear Discriminant Analysis for Enhancing Classification Accuracy and Interpretability
por: Vo, Tuan L., et al.
Publicado: (2024)

ECG-IMN: Interpretable Mesomorphic Neural Networks for 12-Lead Electrocardiogram Interpretation
por: Thambawita, Vajira, et al.
Publicado: (2026)

Missing data imputation for noisy time-series data and applications in healthcare
por: Le, Lien P., et al.
Publicado: (2024)

Optimal Transport for Structure Learning Under Missing Data
por: Vo, Vy, et al.
Publicado: (2024)

Structured Exploration and Exploitation of Label Functions for Automated Data Annotation
por: Lam, Phong, et al.
Publicado: (2026)

Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
por: Nguyen, Duong M., et al.
Publicado: (2025)

Calliope: A TTS-based Narrated E-book Creator Ensuring Exact Synchronization, Privacy, and Layout Fidelity
por: Hammer, Hugo L., et al.
Publicado: (2026)

Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction
por: Do, Tu T., et al.
Publicado: (2023)

Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption
por: Nik, Alireza, et al.
Publicado: (2025)

Position: AI Security Policy Should Target Systems, Not Models
por: Riegler, Michael A., et al.
Publicado: (2026)

Divide and Refine: Enhancing Multimodal Representation and Explainability for Emotion Recognition in Conversation
por: Mai, Anh-Tuan, et al.
Publicado: (2026)

Uncertainty-aware Traffic Prediction under Missing Data
por: Mei, Hao, et al.
Publicado: (2023)

Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data
por: Lammers, Kathrin, et al.
Publicado: (2025)

shapiq: Shapley Interactions for Machine Learning
por: Muschalik, Maximilian, et al.
Publicado: (2024)

On the Relationship Between Interpretability and Explainability in Machine Learning
por: Leblanc, Benjamin, et al.
Publicado: (2023)

Investigating the Duality of Interpretability and Explainability in Machine Learning
por: Garouani, Moncef, et al.
Publicado: (2025)

Explainable Machine Learning for ICU Readmission Prediction
por: de Sá, Alex G. C., et al.
Publicado: (2023)

Explainability for Machine Learning Models: From Data Adaptability to User Perception
por: Delaunay, julien
Publicado: (2024)

Robust Simulation-Based Inference under Missing Data via Neural Processes
por: Verma, Yogesh, et al.
Publicado: (2025)

Analyzing the Impact of Adversarial Examples on Explainable Machine Learning
por: Devabhakthini, Prathyusha, et al.
Publicado: (2023)

Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs
por: Wendland, Joshua, et al.
Publicado: (2026)

Machine Unlearning under Overparameterization
por: Block, Jacob L., et al.
Publicado: (2025)

Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review
por: Lopez-Ramos, Luis M., et al.
Publicado: (2024)

Subjectivity in Unsupervised Machine Learning Model Selection
por: Chen, Wanyi, et al.
Publicado: (2023)

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
por: Krishna, Satyapriya, et al.
Publicado: (2022)

An Explainable Machine Learning Framework for the Accurate Diagnosis of Ovarian Cancer
por: Newaz, Asif, et al.
Publicado: (2023)

Explainable Machine Learning Framework for Cardiovascular Disease Diagnosis and Prognosis
por: Sourov, Md. Emon Akter, et al.
Publicado: (2025)

Missing Data Multiple Imputation for Tabular Q-Learning in Online RL
por: Chasalow, Kyla, et al.
Publicado: (2025)

Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics
por: Munoz, Cristian, et al.
Publicado: (2023)

Matching Problems to Solutions: An Explainable Way of Solving Machine Learning Problems
por: Saleh, Lokman, et al.
Publicado: (2024)

A Comprehensive Perspective on Explainable AI across the Machine Learning Workflow
por: Paterakis, George, et al.
Publicado: (2025)

Large Language Models as Model Organisms for Human Associative Learning
por: Kolling, Camila, et al.
Publicado: (2025)

Data Heterogeneity Modeling for Trustworthy Machine Learning
por: Liu, Jiashuo, et al.
Publicado: (2025)

FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection
por: Nguyen, Van Tuan, et al.
Publicado: (2024)

Verifying Machine Unlearning with Explainable AI
por: Vidal, Àlex Pujol, et al.
Publicado: (2024)