Imagen de Portada

Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Böcking, Lars, Müller, Leopold, Kühl, Niklas
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

Do Edges Matter? Investigating Edge-Enhanced Pre-Training for Medical Image Segmentation
por: Zaha, Paul, et al.
Publicado: (2025)

A Multivocal Literature Review on Privacy and Fairness in Federated Learning
por: Balbierer, Beatrice, et al.
Publicado: (2024)

CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
por: Zipperling, Domenique, et al.
Publicado: (2024)

Evaluating Simplification Algorithms for Interpretability of Time Series Classification
por: Håvardstun, Brigt, et al.
Publicado: (2025)

Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction
por: Ball, Sarah, et al.
Publicado: (2025)

Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification
por: Liu, Ziyu, et al.
Publicado: (2024)

PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology
por: Pham, Anh-Duy, et al.
Publicado: (2025)

Privacy-Preserving Dynamic Assortment Selection
por: Cho, Young Hyun, et al.
Publicado: (2024)

Meta-learning to Address Data Shift in Time Series Classification
por: Myren, Samuel, et al.
Publicado: (2026)

Preserving Temporal Dynamics in Time Series Generation
por: Lin, Ci, et al.
Publicado: (2026)

Operationalizing Data Minimization for Privacy-Preserving LLM Prompting
por: Zhou, Jijie, et al.
Publicado: (2025)

Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
por: Liu, Rui, et al.
Publicado: (2025)

EnergyPatchTST: Multi-scale Time Series Transformers with Uncertainty Estimation for Energy Forecasting
por: Li, Wei, et al.
Publicado: (2025)

Multi-Horizon Time Series Forecasting of non-parametric CDFs with Deep Lattice Networks
por: Erdmann, Niklas, et al.
Publicado: (2025)

Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
por: Zhou, Xinjie, et al.
Publicado: (2026)

Towards a Problem-Oriented Domain Adaptation Framework for Machine Learning
por: Spitzer, Philipp, et al.
Publicado: (2025)

Improving Epidemic Analyses with Privacy-Preserving Integration of Sensitive Data
por: Guan, Zihan, et al.
Publicado: (2025)

Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
por: Wang, Kaizheng, et al.
Publicado: (2024)

StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning
por: Shi, Tongjun, et al.
Publicado: (2024)

Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
por: Koutsoubis, Nikolas, et al.
Publicado: (2024)

Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms
por: Farahani, Mojtaba A., et al.
Publicado: (2023)

A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets
por: Elhalwagy, Ayman, et al.
Publicado: (2023)

Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
por: Osuala, Richard, et al.
Publicado: (2024)

Performative Time-Series Forecasting
por: Zhao, Zhiyuan, et al.
Publicado: (2023)

POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective
por: Chen, Shaowu, et al.
Publicado: (2023)

CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
por: Xie, Shifeng, et al.
Publicado: (2025)

BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks
por: Yadav, Sushilkumar, et al.
Publicado: (2024)

Semi-Periodic Activation for Time Series Classification
por: Júnior, José Gilberto Barbosa de Medeiros, et al.
Publicado: (2024)

Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
por: Wang, Yijun, et al.
Publicado: (2026)

What-If Explanations Over Time: Counterfactuals for Time Series Classification
por: Schlegel, Udo, et al.
Publicado: (2026)

CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
por: Koumar, Josef, et al.
Publicado: (2024)

Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification
por: Lee, Jiseok, et al.
Publicado: (2024)

CRITS: Convolutional Rectifier for Interpretable Time Series Classification
por: Kuratomi, Alejandro, et al.
Publicado: (2025)

Mechanistic Interpretability for Transformer-based Time Series Classification
por: Kalnāre, Matīss, et al.
Publicado: (2025)

ST-Tree with Interpretability for Multivariate Time Series Classification
por: Du, Mingsen, et al.
Publicado: (2024)

CAFO: Feature-Centric Explanation on Time Series Classification
por: Kim, Jaeho, et al.
Publicado: (2024)

MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
por: Feofanov, Vasilii, et al.
Publicado: (2026)

PAC Privacy Preserving Diffusion Models
por: Xu, Qipan, et al.
Publicado: (2023)

Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
por: Gong, Peiliang, et al.
Publicado: (2025)

BinaryShield: Cross-Service Threat Intelligence in LLM Services using Privacy-Preserving Fingerprints
por: Gill, Waris, et al.
Publicado: (2025)