CHI-ANNOUNCEMENTS Archives

ACM SIGCHI General Interest Announcements (Mailing List)

CHI-ANNOUNCEMENTS@LISTSERV.ACM.ORG

Options: Use Forum View

Use Monospaced Font
Show HTML Part by Default
Show All Mail Headers

Message: [<< First] [< Prev] [Next >] [Last >>]
Topic: [<< First] [< Prev] [Next >] [Last >>]
Author: [<< First] [< Prev] [Next >] [Last >>]

Print Reply
Subject:
From:
Vito Walter Anelli <[log in to unmask]>
Reply To:
Vito Walter Anelli <[log in to unmask]>
Date:
Mon, 7 Jun 2021 15:37:57 +0000
Content-Type:
text/plain
Parts/Attachments:
text/plain (86 lines)
Call for Papers "The 1st International Workshop on Adversarial Machine Learning for Recommendation and Search (AdveRSe 2021)"

WebSite: https://sisinflab.github.io/adverse2021/cfp/
Date: 1-5 November 2021, Online Hosted in Gold Coast, Queensland, Australia

Abstract Submission deadline: July 3rd, 2021, 2021 AoE
Paper Submission deadline: July 10th, 2021, 2021 AoE


[Scope]
Recently, research in adversarial machine learning has brought to light important potential security issues with systems that people use on a daily basis for search and discovery. While adversarial examples are well understood in computer vision tasks, the harmful effects of the malicious application of machine learning are less well-understood in information retrieval and recommendation systems. The issues include: injection of adversarial-crafted fake users, adversarial perturbation of multimedia data in training sets or background collections, and adversarial structural noise on graph structure in order to improve search and recommendation in real-world environments, research is necessary that will allow us to discover, understand, and control the adverse impact of adversarial machine learning. In this workshop, we aim to bring together researchers from the fields of adversarial machine learning, information retrieval, and recommender systems to discuss recent advances and research directions that could be further exploited to broaden the frontier in the field.

The purpose of the 1st International Workshop on Adversarial Machine Learning for Recommendation and Search (AdveRSe 2021) is to provide a meeting forum for stimulating and disseminating research in Adversarial Machine Learning for Recommendation and Search Systems, where researchers can network and discuss their research results in an informal way.
The Half-Day Workshop will take place online on November 1-5, 2021 in conjunction with the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), hosted in Gold Coast, Queensland, Australia.

[Submission Guidelines]

Submissions of full research papers must be in English, in PDF format in the CEUR-WS two-column conference format available at CEURART or at  OVERLEAF TEMPLATE if an Overleaf is preferred.
    • Long Papers should report on substantial contributions of lasting value. The Long papers must have a length of minimum 6 and maximum 8 pages (plus an unlimited number of pages for references). Each accepted long paper will be included in the CEUR on-line Workshop proceedings and presented in a plenary session as part of the Workshop program.
    • Short/Position Papers typically discuss exciting new work that is not yet mature enough for a long paper. In particular, novel but significant proposals will be considered for acceptance to this category despite not having gone through sufficient experimental validation or lacking strong theoretical foundation. Applications of adversarial learning in recommendation and search systems to novel areas are especially welcome. The Short Papers must have a length of minimum 3 and maximum 5 pages (plus an unlimited number of pages for references). Each accepted long paper will be included in the CEUR on-line Workshop proceedings and presented in a plenary session as part of the Workshop program

To be considered, papers must be received by the submission deadline (see Important Dates). The short and long papers review process is double-blind. The authors must anonymize their submissions. Submitted papers will be evaluated according to their originality, technical content, style, clarity, and relevance to the workshop. Short and long paper submissions must be original work and may not be under submission to other venues at the time of review (authors can upload to institutional or other preprint repositories such as arXiv.org before reviewing is complete).

[Topics]
AdveRSe 2021 offers an opportunity to present and discuss both theoretical and empirical research. Relevant topics include, but are not restricted to:
    • Theory and algorithms for AML in search and recommendation
        ◦ Attacks on images, videos, audio signals, and text
        ◦ Attacks on model parameters with gradient methods
        ◦ Attacks by generating of fake profiles, e.g., customer one-commerce platforms
        ◦ Attacks crafted with generative adversarial networks
        ◦ Real-world attack scenarios–Other attack techniques
    • Theory and algorithms for defending with AML in search and recommendation
        ◦ Robust optimization methods
        ◦ Adversarial training strategies
        ◦ Robustness certification
        ◦ Generative adversarial networks to protect from adversarial attacks
        ◦ Data augmentation with adversarial training
        ◦ Datasets for evaluating model robustness
    • Evaluation of Adversarial Attacks and Defences in search and recommendation
        ◦ Offline evaluation measures and protocols
        ◦ Online evaluation measures and protocols
        ◦ Advanced applications (Trustworthy ML) for search and recommendation
        ◦ Fairness
        ◦ Privacy
        ◦ Federated machine learning
        ◦ Explainable machine learning models
        ◦ Causal and Counterfactual reasoning

[Important Dates]
    • Submission website opens: May 26, 2021
    • Abstract registration deadline: July 3, 2021
    • Submission deadline: July 10, 2021
    • Notification of acceptance: August 10, 2021
    • Camera ready deadline: TBA
    • AdvRSe 2021: November 1-5, 2021

[Organizers]
    • Battista, Biggio, University of Cagliari, Italy, [log in to unmask]
    • Felice Antonio, Merra, Politecnico di Bari, Italy, [log in to unmask]
    • Julian, McAuley, UC San Diego, United States, [log in to unmask]
    • Martha, Larson, Radboud University, Netherlands, [log in to unmask]
    • Tommaso,Di Noia, Politecnico di Bari, Italy, [log in to unmask]
    • Vito Walter, Anelli, Politecnico di Bari, Italy, [log in to unmask]
    • Xiangnan, He, University of Science and Technology of China, China, [log in to unmask]
    • Yashar, Deldjoo, Politecnico di Bari, Italy, [log in to unmask]

[Publication]
AdveRSe2021 proceedings will be published by CEUR Workshop Proceedings.

[Venue]
The conference will be held in conjunction with the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), hosted in Gold Coast, Queensland, Australia.

[Contact]
All questions about submissions should be emailed to the contact authors
    • Felice Antonio, Merra, Politecnico di Bari, Italy, [log in to unmask]
    • Tommaso,Di Noia, Politecnico di Bari, Italy, [log in to unmask]
Informativa Privacy - Ai sensi del Regolamento (UE) 2016/679 si precisa che le informazioni contenute in questo messaggio sono riservate e ad uso esclusivo del destinatario. Qualora il messaggio in parola Le fosse pervenuto per errore, La preghiamo di eliminarlo senza copiarlo e di non inoltrarlo a terzi, dandocene gentilmente comunicazione. Grazie. Privacy Information - This message, for the Regulation (UE) 2016/679, may contain confidential and/or privileged information. If you are not the addressee or authorized to receive this for the addressee, you must not use, copy, disclose or take any action based on this message or any information herein. If you have received this message in error, please advise the sender immediately by reply e-mail and delete this message. Thank you for your cooperation.

    ----------------------------------------------------------------------------------------
    To unsubscribe from CHI-ANNOUNCEMENTS send an email to:
     mailto:[log in to unmask]

    To manage your SIGCHI Mailing lists or read our polices see:
     https://sigchi.org/operations/listserv/
    ----------------------------------------------------------------------------------------

ATOM RSS1 RSS2