Machine Learning and Principles and Practice of Knowledge Discovery in Databases International Workshops of ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Revised Selected Papers, Part III

.- Workshop on Machine Learning for Sustainable Power Systems (ML4SPS 2025)

.- Can A.I. Revolutionize EV Dispatch?.



.- Revealing the empirical flexibility of gas units through deep clustering.



.- A Real-World Deployment of Federated Learning for Residential Solar PV Power Forecasting.



.- Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation.



.- Workshop on Safe Reinforcement Learning for V2G-enabled Electric Vehicle Aggregators.



.- PPTopoGym: Towards an RL Environment for Topology Actions on Power Grids.



.- Transfer learning and uncertainty estimation for data-driven battery state of health estimation.



.- VPP-Sim: A Modular Open-Source Framework for Developing and Deploying ML-Driven Strategies in Virtual Power Plants.

.- Workshop on Synthetic Data for AI Trustworthiness and Evolution (SynDAiTE 2025)

.- Synthetic Non-stationary Data Streams for Recognition of the Unknown.



.- cPB: Continuous Piggyback for Streaming Continual Learning with Temporal Dependence.



.- Can we Evaluate RAGs with Synthetic Data?.



.- DriftMoE: Streaming Mixture of Experts for Adaptive Learning under Concept Drift.



.- TAGAL: Tabular Data Generation using Agentic LLM Methods.



.- Adapting Stable Diffusion Models for Domain-Specific Medical Imaging: A Case Study in Synthetic Retinal Fundus Image Generation.



.- SMLT: A Synthetic Dataset for Stealthy Manipulation of Energy Market via False Data Injection Attacks.



.- Generating Censored Data with Controlled and Real-World-Like Properties.



.- ReL8r: A New Benchmarking Framework for Tabular Data Generators Using Constructed Relationships.



.- Style Transfer for High-Fidelity Time Series Augmentation.



.- Enabling Granular Subgroup Level Model Evaluations by Generating Synthetic Medical Time Series.



.- Evaluating Predictive Maintenance Models in the Presence of Reflexivity: A Case Study in Pharmaceutical Manufacturing.



.- Mitigating Dataset Shift via Smart Augmentation with Conditional Diffusion Models.



.- SPATA: Systematic Pattern Analysis for Detailed and Transparent Data Cards.



.- Enhancing Synthetic Data Realism for Autonomous Vehicles Using Segmentation-Guided ControlNet.

.- Workshop on MIning Data for Financial Applications (MIDAS 2025)

.- LARK: Integrating LLM-based KG Construction and RAG for Financial Question Answering.



.- Does Improving Forecasting Accuracy Also Improves Financial Utility? A Case Study with Binary Options.

.- Workshop on Advancements in Federated Learning (WAFL 2025)

.- Decentralized Time Series Classification with ROCKET Features.



.- FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning.



.- Federated Learning of AnDE Classifiers.



.- Federated Learning with Heterogeneous and Private Label Sets.



.- FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting.



.- Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering.



.- TVFed-P: Tversky-based Federated Learning with Personalized Loss Parameterization for Medical Imbalanced Data.



.- Personalized Aggregation for Federated Prototypical Learning.

.- Workshop on Mining and Learning with Graphs (MLG 2025)

.- Contrastive Learning as Optimal Homophilic Graph Structure Learning.



.- From Pixels to Graphs: Deep Graph-Level Anomaly Detection on Dermoscopic Images.



.- Graph Product Representations.



.- Late and Early Fusion Graph Neural Network Architectures for Integrative Modeling of Multimodal Brain Connectivity Graphs.



.- Task-Agnostic Contrastive Pretraining for Relational Deep Learning.



.- A Spatio-Temporal Transformer Model for Node Attribute Prediction in Dynamic Graphs.



.- Do We Need Curved Spaces? A Critical Look at Hyperbolic Graph Learning in Graph Classification.



.- Iterative Graph-Based Radius Constraint Clustering.