Description
The 5-volume set CCIS 2839 2843 constitutes the refereed proceedings of several workshops held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025, which took place in Porto, Portugal, in September 2025.
The 236 full papers included in these proceedings were carefully reviewed and selected from 413 submissions. The papers were organized topical sections as follows:
Part I: Workshop on Data Science for Social Good SoGood 2025), Workshop on Bias and Fairness in AI (BIAS 2025), Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2025), Human-Centered Data Mining Workshop (HuMine 2025) and Workshop on Data-Centric Artificial Intelligence (DEARING 2025).
Part II: Workshop on Hybrid Human-Machine Learning and Decision Making (HLDM 2025), Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2025), Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2025),Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2025), Workshop on Deep Learning Meets Neuromorphic Hardware (DLmNH 2025), Machine Learning for Cybersecurity (MLCS 2025),AI for Safety-Critical Infrastructures (AI-SCI 2025) and Workshop on Innovations, Privacy-preservation, and Evaluations of Machine Unlearning Techniques (WIPE-OUT).
Part III: Workshop on Machine Learning for Sustainable Power Systems (ML4SPS 2025), Workshop on Synthetic Data for AI Trustworthiness and Evolution (SynDAiTE 2025), Workshop on MIning Data for Financial Applications (MIDAS 2025), Workshop on Advancements in Federated Learning (WAFL 2025) and Workshop on Mining and Learning with Graphs (MLG 2025).
Part IV: Workshop on Interactive Adaptive Learning (IAL 2025), Workshop on Machine Learning for Irregular Time Series (ML4ITS 2025), Interactive eXplainable AI, Theory and Practice (IXAIT 2025), Workshop on Learning on Real and Synthetic Medical Time Series Data (MED-TIME 2025), Workshop on Responsible Healthcare Using Machine Learning (RHCML 2025), Workshop for Explainable AI in Time Series and Data Streams (TempXAI 2025) and Workshop on Explainable Knowledge Discovery in Data Mining and Unlearning (XKDD 2025).
Part V: Workshop on Learning from Small Data (LFSD 2025), Workshop on Machine Learning for Earth Observation (MACLEAN 2025), Workshop on Artificial Intelligence, Data Analytics and Democracy (AIDEM 2025) and Discovery Challenges.
.- 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 Gr
