WIPE-OUT 2025 : Workshop on Innovations, Privacy-preservation, and Evaluations Of machine Unlearning Techniques

*** Apologies for cross-postings ***
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[DEADLINE EXTENDED!] Call For Papers
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Workshop on Innovations, Privacy-preservation, and Evaluations Of
machine Unlearning Techniques (WIPE-OUT 2025) to be held as
part of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECMLPKDD2025).
Workshop proceedings will be published as indexed post-proceedings
volume of Springer’s Lecture Notes in Computer Science (LNCS).
Date: September 15th, 2025 - Porto (Portugal)
Web: https://aiimlab.org/short/WIPE-OUT_2025
Submission Website:
https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FECMLPKDDWorkshopTrack2025%2FSubmission%2FIndex
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Important Dates
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Paper Submissions deadline: June 7, 2025 -) June 22, 2025
Notifications: July 7th, 2025
Camera-Ready: July 21st, 2025
Workshop: September 15th, 2025
All deadlines are 11:59 pm AoE
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Workshop Aims and Scope
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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 the 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!
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Workshop Keywords
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Machine Unlearning · Security and Privacy · Ethical AI · Model Editing
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Workshop Topics
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WIPE-OUT welcomes 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, and streaming data.). Contributions can also include research or
perspectives regarding the following:
* Foundations of Machine Unlearning:
– Theoretical foundations, guarantees, and bounds for MU;
– 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, …);
* Beyond Machine Unlearning:
– Privacy Preservation and Differential Privacy Mechanisms;
– Model Editing and Online Learning Techniques;
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Submission and Publication
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We invite authors to submit unpublished, original papers written in English.
Submitted papers should not have been previously published or accepted for
publication in a substantially similar form in any peer-reviewed venue, such as journals,
conferences, or workshops. The authors should consult Springer’s authors’ guidelines
and use their proceedings templates, either LaTeX or Word. The submission
portal will be the same as the Main Conference and has TBA yet. 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/Replicability 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.
Publication The Workshop papers will be included in an indexed post-workshop proceeding volume published by Springer Communications in Computer and Information Science (LNCS).
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Registration and Presentation Policy
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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/
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Workshop Chairs
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Andrea D’Angelo
University of L’Aquila, L’Aquila (Italy)
Email: [email protected]
Claudio Savelli
Polytechnic of Turin, Turin (Italy)
Email: [email protected]
Flavio Giobergia
Polytechnic of Turin, Turin (Italy)
Email: [email protected]
Francesco Gullo
University of L’Aquila, L’Aquila (Italy)
Email: [email protected]
Giovanni Stilo
University of L’Aquila, L’Aquila (Italy)
Email: [email protected]
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Contacts
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For general inquiries about the workshop, please email
[email protected], [email protected],
[email protected], [email protected], and [email protected] .