Workshop on Innovations, Privacy-preservation, and Evaluations Of machine Unlearning Techniques (WIPE-OUT 2025)
Workshop at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2025), on 15-19 September 2025 (Porto, Portugal)
Organisers
Andrea D'Angelo
Claudio Savelli
Dr. Flavio Giobergia
Francesco Gullo
Prof. Giovanni Stilo
Table of contents
- Aims and Scope
- Important Dates
- Topics of Interests
- Registration and Presentation Policy
- Submission and Publication
Aims and Scope
As AI adoption soars, so do concerns over data privacy, ethics, and regulatory compliance (e.g., GDPR, AI Act, CCPA, Bill C-27). Machine Unlearning (MU) emerges as a game-changer, enabling selective removal of learned information without costly retraining—mitigating biases, protecting sensitive data, and aligning AI with ethical standards.
WIPE-OUT (as ECML-PKDD Workshop) brings together pioneers in MU to push boundaries in privacy-preserving AI. From cutting-edge algorithms to real-world applications, we foster collaboration to tackle the legal, technical, and ethical challenges of unlearning. Join us in redefining the AI landscape! 🚀
Important Dates
- Paper Submissions: June 7th, 2025
- Notifications: July 7th, 2025
- Camera-Ready: July 21st, 2025
- Workshop: September 15th, 2025
All deadlines are 11:59 pm AoE.
Topics of Interests
WIPE-OUT Workshop welcomes research and perspective contributions on all topics related to Machine Unlearning across domains (e.g., finance, business, basic sciences, construction computational advertising, medical, etc.) and independent of data types (e.g., networks, tabular, unstructured, graphs, logs, spatiotemporal, multimedia, time series, genomic sequences, streaming data, etc. ). Contributions can also include research or perspectives regarding the following:
- Foundations of Machine Unlearning
- Theoretical foundations, guarantees, and bounds for Machine Unlearning
- Development of new Machine Unlearning Algorithms
- Efficient and Scalable Machine Unlearning in Big Data Systems
- Applications of Machine Unlearning
- Real-Time and Streaming Data Unlearning Systems
- Geometrical Machine Unlearning
- Machine Unlearning for Language and Other Foundation Models
- Federated and Decentralized Unlearning Processes
- Evaluation and benchmarking
- Evaluation Metrics for quantifying unlearning performance
- Development of new Evaluation protocol for Machine Unlearning
- Tools and Benchmarks for Unlearning Framework
- Implications of Machine Unlearning
- Unlearning for Explainable and Interpretable AI
- Ethical, legal and societal aspects of Machine Unlearning
- Machine Unlearning in High-Stakes Applications (e.g., healthcare, finance, …)
- Beyond Machine Unlearning
- Privacy Preservation and Differential Privacy Mechanisms
- Model Editing and Online Learning Techniques
Submission and Publication
We invite authors to submit unpublished, original papers written in English. Submitted papers should not have been previously published or accepted for publication in substantially similar form in any peer-reviewed venue, such as journals, conferences, or workshops. The authors should consult Springer’s authors’ guidelines and use the ECML-PKDD proceedings author kit Springer LNCS Template.
We will consider three different submission types:
- Full papers (up to 14 pages) should clearly describe the state of the art and state the proposal's contribution in the application domain, even if presenting preliminary results. In particular, research papers should describe the methodology in detail, experiments should be repeatable, and a comparison with the existing approaches in the literature should be made.
- Reproducibility papers (up to 14 pages) should repeat prior experiments using the original source code and datasets to show how, why, and when the methods work or not (replicability papers) or should repeat prior experiments, preferably using the original source code in new contexts (e.g., different domains and datasets, different evaluation and metrics) to generalize further and validate or not previous work (reproducibility papers).
- Short, Demo, and position papers (up to 8 pages) should introduce new points of view on the workshop topics or summarize a group's experience in the field. Practice and experience reports must detail real-world scenarios in which Machine Unlearning is needed.
Submissions should not exceed the indicated pages, including any diagrams and references. All submissions will undergo a double-blind review process and be reviewed by at least three reviewers based on relevance to the workshop, novelty/originality, significance, technical quality and correctness, quality and clarity of presentation, quality of references, and reproducibility. Submitted papers will be rejected without review if they are not correctly anonymized, do not comply with the template, or do not follow the above guidelines.
Generative AI Usage Policy. Generative AI models, including Chat-GPT, BARD, LLaMA, or similar LLMs, do not satisfy the criteria for authorship of papers accepted in the workshop. If authors use an LLM in any part of the paper-writing process, they assume full responsibility for all content, including checking for plagiarism and correctness of all text.
Proceedings. The accepted papers and the material generated during the meeting will be available on the workshop website. The workshop proceedings will be published as a post-proceedings volume of Springer’s Lecture Notes in Computer Science (LNCS), indexed on Google Scholar, DBLP, and Scopus. The authors of selected papers may be invited to submit an extended version to a special journal issue.
Registration and Presentation Policy
Please be aware that at least one author per paper must register and attend the workshop to present the work. We expect the authors, the program committee, and the organizing committee to adhere to the ECML-PKDD Code of Conduct.
The Main Conference organization team will manage the registration: https://ecmlpkdd.org/2025/
Contacts
For general inquiries about the workshop, please email andrea.dangelo6@graduate.univaq.it, claudio.savelli@polito.it, flavio.giobergia@polito.it, gullof@acm.org, and giovanni.stilo@univaq.it.