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ORCIDPKDD / ECML 2025: Porto, Portugal - Part II
- Rita P. Ribeiro
, Bernhard Pfahringer, Nathalie Japkowicz, Pedro Larrañaga, Alípio M. Jorge, Carlos Soares, Pedro H. Abreu, João Gama:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part II. Lecture Notes in Computer Science 16014, Springer 2026, ISBN 978-3-032-05980-2
Data Challenges
- Runxue Bao, Quanchao Lu, Yanfu Zhang:
Safe Screening Rules for Group SLOPE. 3-19 - Denis Huseljic, Marek Herde, Lukas Rauch, Paul Hahn, Zhixin Huang, Daniel Kottke, Stephan Vogt, Bernhard Sick:
Efficient Bayesian Updates for Deep Active Learning via Laplace Approximations. 20-35 - Frederik Boe Hüttel, Christoffer Riis, Filipe Rodrigues, Francisco C. Pereira:
Bayesian Active Learning for Censored Regression. 36-51 - Hideaki Ishibashi, Kota Matsui, Kentaro Kutsukake, Hideitsu Hino:
An (ε ,δ )-Accurate Level Set Estimation with a Stopping Criterion. 52-69 - Yannick Rudolph, Kai Neubauer, Ulf Brefeld:
Self-improvement for Computerized Adaptive Testing. 70-86 - Yang Xu, Kai Ming Ting:
Voronoi Diagram Encoded Hashing. 87-103 - Xiaohui Yu, Qiao Yan:
Adaptive Multi-space Defense Framework Against Adversarial Attacks. 104-118 - Pawel Zyblewski, Szymon Wojciechowski:
How to RETIRE Tabular Data in Favor of Discrete Digital Signal Representation. 119-135
Diffusion Models
- Rushan Geng, Ge Chen, Cuicui Luo:
Diffusion Model with Selective Attention for Temporal Knowledge Graph Reasoning. 139-155 - Rongshen He, Abubakar Zakari, Qinru Yang, Jiaqi Luo, Changsheng Ma:
Topology-Aware Hierarchical Graph Diffusion Model for Molecular Graph Generation. 156-172 - Chi Hong, Jiyue Huang, Robert Birke, Dick H. J. Epema, Stefanie Roos, Lydia Y. Chen:
Single-Fold Distillation for Diffusion Models. 173-189 - Dibyanshu Kumar, Philipp Väth, Magda Gregorová:
Loss Functions in Diffusion Models: A Comparative Study. 190-205 - Philipp Väth, Dibyanshu Kumar, Benjamin Paassen, Magda Gregorová:
Diffusion Classifier Guidance for Non-robust Classifiers. 206-221 - Alexandre Verine, Mehdi Inane, Florian Le Bronnec, Benjamin Négrevergne, Yann Chevaleyre:
Improving Discriminator Guidance in Diffusion Models. 222-238 - Wendong Zhang, Haoqi Chen, Song Yu:
JKDM: A Joint Structural and Semantic Diffusion-Generated Knowledge Completion Model. 239-255
Ensemble Learning
- Grigor Bezirganyan, Sana Sellami, Laure Berti-Équille, Sébastien Fournier:
EM-SEC: Efficient Multi-head Set-Valued Evidential Classification. 259-277 - Hefei Liang, Jiaqi Liu, Bin Guo, Zhiwen Yu:
A Complementarity-Enhanced Mixture of Human-AI Teams for Decision-Making. 278-293 - Kelin Liu, Yao Zhou, Bin Liu, Hanjing Su, Shouzhi Chen:
MEAN: Multi-Expert Adaptive Network For Customer Lifetime Value Prediction. 294-309 - Denis Lukovnikov, Asja Fischer:
Enabling ControlNet to follow Localized Descriptions Using Cross-Attention Control. 310-327
Federated Learning
- Milena Angelova, Veselka Boeva, Shahrooz Abghari, Selim Ickin, Xiaoyu Lan:
FedCluLearn: Federated Continual Learning Using Stream Micro-cluster Indexing Scheme. 331-349 - Lingxiao Kong, Jiahui Jiang, Haozhao Wang, Lei Wu, Ruixuan Li:
FedRNL: Federated Rationalization with Soft Parameter Sharing. 350-366 - Fan Liu, Siqi Lai, Yansong Ning, Hao Liu:
Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network. 367-383 - Zhi Wen Soi, Chenrui Fan, Aditya Shankar, Abel Malan, Lydia Y. Chen:
Federated Time Series Generation on Feature and Temporally Misaligned Data. 384-399
Graph Neural Networks
- Clemens Damke, Eyke Hüllermeier:
Distribution Matching for Graph Quantification Under Structural Covariate Shift. 403-419 - Yaniv Galron, Fabrizio Frasca, Haggai Maron, Eran Treister, Moshe Eliasof:
Understanding and Improving Laplacian Positional Encodings for Temporal GNNs. 420-437 - Jakub Peleska, Gustav Sír:
ReDeLEx: A Framework for Relational Deep Learning Exploration. 438-456 - Taoyang Qin, Ke-Jia Chen, Zheng Liu:
Localized Heat Kernel for Graph Neural Networks. 457-473 - Takuto Takahashi, Itsuki Nakayama, Takahiro Mitani, Ryosuke Kikuchi, Yuya Sasaki, Makoto Onizuka:
Graph Neural Network Leveraging Higher-Order Class Label Connectivity for Heterophilous Graphs. 474-491 - Karan Vombatkere, Theodoros Lappas, Evimaria Terzi:
A QUBO Framework for Team Formation. 492-510 - Ziyu Wang:
Enhancing Graph Transformers with SNNs and Mutual Information. 511-526 - Donghang Wu, Lian Shen, Changzhi Jiang, Yanhao Li, Xiangrong Liu:
PipeQS: Pipeline-Based Adaptive Quantization and Staleness-Aware Distributed GNN Training System. 527-543
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