:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Perifanis, Vasilis, Nikolaidou, Foteini, Pavlidis, Nikolaos, Thomakos, Panagiotis, Sendros, Andreas
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2605.04993
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction
by: Tsiolakis, Theodoros, et al.
Published: (2024)

Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
by: Pavlidis, Nikolaos, et al.
Published: (2024)

Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets
by: Pavlidis, Nikolaos, et al.
Published: (2025)

Improving Early Sepsis Onset Prediction Through Federated Learning
by: Düsing, Christoph, et al.
Published: (2025)

Chargax: A JAX Accelerated EV Charging Simulator
by: Ponse, Koen, et al.
Published: (2025)

Data-Driven Optimization of EV Charging Station Placement Using Causal Discovery
by: Junker, Julius Stephan, et al.
Published: (2025)

To Charge or to Sell? EV Pack Useful Life Estimation via LSTMs, CNNs, and Autoencoders
by: Bosello, Michael, et al.
Published: (2021)

Exploring Energy Landscapes for Minimal Counterfactual Explanations: Applications in Cybersecurity and Beyond
by: Evangelatos, Spyridon, et al.
Published: (2025)

A Multi-View Multi-Timescale Hypergraph-Empowered Spatiotemporal Framework for EV Charging Forecasting
by: Li, Jinhao, et al.
Published: (2025)

A Safe Deep Reinforcement Learning Approach for Energy Efficient Federated Learning in Wireless Communication Networks
by: Koursioumpas, Nikolaos, et al.
Published: (2023)

An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration
by: Siomos, Vasilis, et al.
Published: (2024)

Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
by: Jin, Jikai, et al.
Published: (2023)

Enabling Delayed-Full Charging Through Transformer-Based Real-Time-to-Departure Modeling for EV Battery Longevity
by: Lee, Yonggeon, et al.
Published: (2025)

Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
by: Huang, Xiannan, et al.
Published: (2026)

Predicting Long Term Sequential Policy Value Using Softer Surrogates
by: Nam, Hyunji, et al.
Published: (2024)

Anomaly Detection in Electric Vehicle Charging Stations Using Federated Learning
by: C, Bishal K, et al.
Published: (2025)

Learning for Interval Prediction of Electricity Demand: A Cluster-based Bootstrapping Approach
by: Dube, Rohit, et al.
Published: (2023)

Enhancing Performance for Highly Imbalanced Medical Data via Data Regularization in a Federated Learning Setting
by: Tsoumplekas, Georgios, et al.
Published: (2024)

Early prediction of the risk of ICU mortality with Deep Federated Learning
by: Randl, Korbinian, et al.
Published: (2022)

Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study
by: Siniosoglou, Ilias, et al.
Published: (2024)

FedPoP: Federated Learning Meets Proof of Participation
by: İşler, Devriş, et al.
Published: (2025)

Improving Operational Efficiency In EV Ridepooling Fleets By Predictive Exploitation of Idle Times
by: Provoost, Jesper C., et al.
Published: (2022)

Preference Learning with Response Time: Robust Losses and Guarantees
by: Sawarni, Ayush, et al.
Published: (2025)

A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
by: Chao, Wenshuo, et al.
Published: (2024)

Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting
by: Turazza, Fabio, et al.
Published: (2026)

Evaluating the Energy Efficiency of NPU-Accelerated Machine Learning Inference on Embedded Microcontrollers
by: Fanariotis, Anastasios, et al.
Published: (2025)

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning
by: Chandrinos, Nikolaos, et al.
Published: (2024)

Early Prediction of Sepsis: Feature-Aligned Transfer Learning
by: Komolafe, Oyindolapo O., et al.
Published: (2025)

Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
by: Li, Yi, et al.
Published: (2024)

CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
by: Yang, Chao, et al.
Published: (2025)

Origin-Destination Demand Prediction: An Urban Radiation and Attraction Perspective
by: Ma, Xuan, et al.
Published: (2024)

Consistency of Neural Causal Partial Identification
by: Tan, Jiyuan, et al.
Published: (2024)

Predicting Survival of Hemodialysis Patients using Federated Learning
by: Raju, Abhiram, et al.
Published: (2024)

A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
by: Park, Young-Jin, et al.
Published: (2024)

A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features
by: Ramachandran, Adithya, et al.
Published: (2026)

Evaluating the Potential of Federated Learning for Maize Leaf Disease Prediction
by: Antico, Thalita Mendonça, et al.
Published: (2024)

Angular Regularization for Positive-Unlabeled Learning on the Hypersphere
by: Sevetlidis, Vasileios, et al.
Published: (2025)

A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes
by: Alzboon, Mowafaq Salem, et al.
Published: (2025)

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
by: Jin, Jikai, et al.
Published: (2025)

Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning
by: Ma, Hui, et al.
Published: (2025)