Table of Contents

• Can A.I. Revolutionize EV Dispatch?Stavros Orfanoudakis, Bob Elders, Peter Palensky, Pedro P. Vergara. 11-25 [doi]
• Revealing the Empirical Flexibility of Gas Units Through Deep ClusteringChiara Fusar Bassini, Alice Lixuan Xu, Jorge Sánchez Canales, Lion Hirth, Lynn H. Kaack. 26-41 [doi]
• A Real-World Deployment of Federated Learning for Residential Solar PV Power ForecastingFrederick Apina, Diogo Monteiro, Bruno Dias, Hugo Morais, Lucas Pereira. 42-57 [doi]
• Study Design and Demystification of Physics Informed Neural Networks for Power Flow SimulationMilad Leyli-Abadi, Antoine Marot, Jérôme Picault. 58-75 [doi]
• Safe Reinforcement Learning for V2G-Enabled Electric Vehicle AggregatorsRuben Eland, Stavros Orfanoudakis, Pedro P. Vergara. 76-91 [doi]
• PPTopoGym: Towards an RL Environment for Topology Actions on Power GridsDominik Köhler, Mohamed Hassouna, Dmitry Degtyar, Jonas Krauß, Kurt Brendlinger, Christoph Scholz 0001. 92-108 [doi]
• Transfer Learning and Uncertainty Estimation for Data-Driven Battery State of Health EstimationAli Beigrezaei, Annelies Vroman. 109-125 [doi]
• VPP-Sim: A Modular Open-Source Framework for Developing and Deploying ML-Driven Strategies in Virtual Power PlantsGian Marco Paldino, Gianluca Bontempi. 126-138 [doi]
• Synthetic Non-stationary Data Streams for Recognition of the UnknownJoanna Komorniczak. 143-159 [doi]
• cPB: Continuous Piggyback for Streaming Continual Learning with Temporal DependenceReza Paki, Federico Giannini, Emanuele Della Valle. 160-176 [doi]
• Can We Evaluate RAGs with Synthetic Data?Jonas van Elburg, Peter van der Putten, Maarten Marx. 177-192 [doi]
• DriftMoE: A Mixture of Experts Approach to Handle Concept DriftsMiguel Aspis, Sebastián A. Cajas Ordóñez, Andrés L. Suárez-Cetrulo, Ricardo Simón Carbajo. 193-207 [doi]
• TAGAL: Tabular Data Generation Using Agentic LLM MethodsBenoît Ronval, Pierre Dupont, Siegfried Nijssen. 208-224 [doi]
• Adapting Stable Diffusion Models for Domain-Specific Medical Imaging: A Case Study in Synthetic Retinal Fundus Image GenerationIvo S. Façoco, Gonçalo Mesquita, Francisca Lúcio, Luís Rosado. 225-240 [doi]
• SMLT: A Synthetic Dataset for Stealthy Manipulation of Energy Market via False Data Injection AttacksGhadeer O. Alsharif, Christos Anagnostopoulos 0001, Angelos K. Marnerides, Mathaios Panteli. 241-256 [doi]
• Generating Censored Data with Controlled and Real-World-Like PropertiesGhanem Bahrini, Morgane Barbet-Massin, Sébastien Razakarivony, Valérie Garès, Jean-François Dupuy. 257-278 [doi]
• ReL8r: A New Benchmarking Framework for Tabular Data Generators Using Constructed RelationshipsMelle Mendikowski, Benjamin Schindler, Thomas Schmid 0003, Ralf Möller 0001, Mattis Hartwig. 279-294 [doi]
• Style Transfer for High-Fidelity Time Series AugmentationMayank Nagda, Phil Ostheimer, Justus Arweiler, Indra Jungjohann, Jennifer Werner, Dennis Wagner, Aparna Muraleedharan, Pouya Jafari, Jochen Schmid, Fabian Jirasek, Jakob Burger, Michael Bortz, Hans Hasse, Stephan Mandt, Marius Kloft, Sophie Fellenz. 295-316 [doi]
• Enabling Granular Subgroup Level Model Evaluations by Generating Synthetic Medical Time SeriesMahmoud K. Ibrahim, Bart Elen, Chang Sun 0001, Gökhan Ertaylan, Michel Dumontier. 317-332 [doi]
• Evaluating Predictive Maintenance Models in the Presence of Reflexivity: A Case Study in Pharmaceutical ManufacturingPedro Sousa, Carlos Soares, Vítor Cerqueira. 333-343 [doi]
• Mitigating Dataset Shift via Smart Augmentation with Conditional Diffusion ModelsLorenzo Peracchio, Gabriele Santangelo, Giovanna Nicora, Barbara Draghi, Allan Tucker, Riccardo Bellazzi, Arianna Dagliati. 344-357 [doi]
• SPATA: Systematic Pattern Analysis for Detailed and Transparent Data CardsJoão Vitorino, Eva Maia, Isabel Praça, Carlos Soares. 358-374 [doi]
• Enhancing Synthetic Data Realism for Autonomous Vehicles Using Segmentation-Guided ControlNetIqra Nosheen, Cathy Ennis, Michael G. Madden. 375-388 [doi]
• LARK: Integrating LLM-Based KG Construction and RAG for Financial Question AnsweringEdward Burgin, Sourav Dutta 0001, MingXue Wang. 393-402 [doi]
• Does Improving Forecasting Accuracy Also Improve Financial Utility? A Case Study with Binary OptionsJosé Santos, Carlos Soares, Paula Branco, Vítor Cerqueira. 403-417 [doi]
• Decentralized Time Series Classification with ROCKET FeaturesBruno Casella, Matthias Jakobs, Marco Aldinucci, Sebastian Buschjäger. 423-451 [doi]
• FedRandom: Sampling Consistent and Accurate Contribution Values in Federated LearningArno Geimer, Beltran Borja Fiz Pontiveros, Radu State. 452-463 [doi]
• Federated Learning of AnDE ClassifiersPablo Torrijos, Juan C. Alfaro, José A. Gámez 0001, José M. Puerta. 464-471 [doi]
• Federated Learning with Heterogeneous and Private Label SetsAdam Breitholtz, Edvin Listo Zec, Fredrik D. Johansson. 472-491 [doi]
• FedABoost: Fairness Aware Federated Learning with Adaptive BoostingTharuka Kasthuri Arachchige, Veselka Boeva, Shahrooz Abghari. 492-507 [doi]
• Robust Federated Learning Under Adversarial Attacks via Loss-Based Client ClusteringEmmanouil Kritharakis, Dusan Jakovetic, Antonios Makris, Konstantinos Tserpes. 508-523 [doi]
• TVFed-P: Tversky-Based Federated Learning with Personalized Loss Parameterization for Medical Imbalanced DataSamar Samir Khalil, Noha S. Tawfik, Marco Spruit. 524-537 [doi]
• Personalized Aggregation for Federated Prototypical LearningSamuele Fonio, Bruno Casella, Marco Aldinucci. 538-553 [doi]
• Contrastive Learning as Homophilic Graph Structure LearningPavel Procházka, Michal Mares, Lukás Bajer. 561-575 [doi]
• From Pixels to Graphs: Deep Graph-Level Anomaly Detection on Dermoscopic ImagesDehn Xu, Tim Katzke, Emmanuel Müller. 576-591 [doi]
• Graph Product RepresentationsMaximilian Seeliger, Fabian Jogl, Thomas Gärtner 0001. 592-606 [doi]
• Late and Early Fusion Graph Neural Network Architectures for Integrative Modeling of Multimodal Brain Connectivity GraphsAlessio Comparini, Léa Schmidt, Vanessa Siffredi, Damien Marie, Clara E. James, Jonas Richiardi. 607-622 [doi]
• Task-Agnostic Contrastive Pretraining for Relational Deep LearningJakub Peleska, Gustav Sír. 623-638 [doi]
• A Spatio-Temporal Transformer Model for Node Attribute Prediction in Dynamic GraphsNamrata Banerji, Tanya Y. Berger-Wolf. 639-648 [doi]
• Do We Need Curved Spaces? A Critical Look at Hyperbolic Graph Learning in Graph ClassificationDionisia Naddeo, Tiago Azevedo 0001, Nicola Toschi. 649-664 [doi]
• Iterative Graph-Based Radius-Constrained ClusteringQuentin Haenn, Brice Chardin, Mickaël Baron, Allel HadjAli. 665-676 [doi]