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Call For Papers

We solicit research papers, systematization of knowledge papers, and position papers (see below for details about each of these categories).

Areas of Interest include (but are not limited to):

  • Trustworthy data curation
  • Novel attacks on ML systems
  • Methods for defending against attacks on ML systems
  • Forensic analysis of ML systems
  • Verifying properties of ML systems
  • Securely and safely integrating ML into systems
  • Privacy (e.g., confidentiality, inference privacy, machine unlearning)
  • Fairness
  • Accountability
  • Transparency
  • Interpretability

Important Dates

  • Abstracts due for Paper​ ​​submissions: Wednesday, October 4, 2023 (11:59 PM AoE, UTC-12)
  • Paper​ ​submission: Wednesday, October 11, 2023(11:59 PM AoE, UTC-12)
  • Interactive discussion & revision phase: November 30 - December 12, 2023 (11:59 PM AoE, UTC-12)
  • Paper​ ​notification: Tuesday, December 19, 2023
  • Camera-ready​ ​versions​ of Papers and Abstracts: Date, TBD, 2024
  • Conference: April 9-11, 2024

Submission Details

Submission Categories

Research Papers, up to 12 pages of body text, with unlimited additional space for references and well-marked appendices. These must be well-argued and worthy of publication and​ ​citation,​ ​on​ ​the​ ​topics​ ​above.​ ​Research​ ​papers​ ​must​ ​present​ ​new​ ​work​, evidence, ​or​ ​ideas.

Systematization of Knowledge papers, up to 12 pages of body text, should ​provide​ ​an integration​ ​and​ ​clarification​ ​of​ ​ideas​ ​on​ ​an​ ​established,​ ​major​ ​research​ ​area,​ ​support​ ​or challenge​ ​long-held​ ​beliefs​ ​in​ ​such​ ​an​ ​area​ ​with​ ​compelling​ ​evidence,​ ​or​ ​present​ ​a convincing,​ ​comprehensive​ ​new​ ​taxonomy​ ​of​ ​some​ ​aspect​ ​of​ secure and trustworthy machine learning. When submitting a systematization of knowledge paper, prepend “SoK:” to the title of your paper on OpenReview.

Position​ ​papers​ ​with​ ​novel visions, with a minimum of 5 pages of body text, ​will​ ​also​ ​be​ ​considered.​ Reviewers will be asked to evaluate vision as bringing opinions and views that pertain to issues of broad interest to the computing community, typically, but not exclusively, of a nontechnical nature. Controversial issues will not be avoided but be dealt with fairly. Authors are welcome to submit carefully reasoned “Viewpoints” in which positions are substantiated by facts or principled arguments. Vision may relate to the wide and abundant spectrum of the computing field of trustworthy machine learning—its open challenges, technical visions and perspectives, educational aspects, societal impact, significant applications and research results of high significance and broad interest. Position papers should set the background and provide introductory references, define fundamental concepts, compare alternate approaches, and explain the significance or application of a particular technology or result by means of well-reasoned text and pertinent graphical material. The use of sidebars to illustrate significant points is encouraged. When submitting a position paper, prepend “Position:” to the title of your paper on OpenReview.

Review Information

While a paper is under submission to this conference, authors may choose to give talks about their work, post a preprint of the paper online, and disclose security vulnerabilities to vendors.

To improve the fairness of the reviewing process, SaTML will follow a double-blind reviewing process. Submitted papers must (a) omit any reference to the authors’ names or the names of their institutions, and (b) reference the authors’ own related work in the third person (e.g., not “We build on our previous work …” but rather “We build on the work of …”). Nothing should be done in the name of anonymity that weakens the submission or makes the job of reviewing the paper more difficult (e.g., important background references should not be omitted or anonymized). Please see this double-blind FAQ for the answers to many common concerns about SaTML’s double-blind reviewing process. When in doubt, contact the program chairs. We are devoted to seeking broad representation in the program, and may take this into account when reviewing multiple submissions from the same author.

To decrease the load of reviewing on PC members, SaTML implements a two-round reviewing process. Each paper is initially assigned two reviews. If the PC chairs conclude that there is no path for acceptance at SaTML upon considering these initial reviews, the paper is early-rejected. This means that the paper is not assigned additional reviews and the authors are notified that their paper will not be included in the conference.

Reviews from Prior Submissions

For papers that were previously submitted to, and rejected from, another conference, authors are required to append to their submission PDF the (anonymized, but otherwise unedited) prior reviews along with a description of how those reviews were addressed in the current version of the paper. Authors are only required to include reviews from the last time the paper was submitted. Authors who try to circumvent this rule (e.g., by changing the title of the paper without significantly changing the contents) may have their papers rejected without further consideration, at the discretion of the PC chairs.

Submissions must use the two-column IEEE Proceedings style: https://www.ieee.org/conferences/publishing/templates.html (Note: use \documentclass[conference]{IEEEtran} for your submission)

Failure to adhere to these rules is grounds for rejection.

Submission Decisions

For each submission, one of the following decisions will be made:

  • Accept: Papers in this category will be accepted for publication in the proceedings and presentation at the conference, possibly after making minor changes with the oversight of a shepherd (Minor Revision). Within one month of acceptance, all accepted papers must submit a camera-ready copy incorporating reviewer feedback. The papers will immediately be published, open access, in the Computer Society’s Digital Library, and they may be cited as “To appear in the IEEE Conference on Secure and Trustworthy Machine Learning, 2024”.

  • Major Revision: A limited number of papers will be invited to submit a major revision; such papers will receive a detailed summary of expectations for revision, in addition to standard reviewer comments. Authors will have a limited time window to submit revision after the notification is sent. The authors should clearly explain in a well-marked appendix how the revisions address the comments of the reviewers. The revised paper will then be re-evaluated, and either accepted or rejected. We will assign the same set of reviewers. Authors can choose to withdraw their paper and not submit a revision.

  • Reject: Papers in this category are declined for inclusion in the conference.

Rebuttal Period

We will have a rebuttal period during which authors have the opportunity to exchange messages with the reviewers, respond to questions asked, and address reviewer comments in the paper. To this end, we will use an anonymous communication feature to enable communication between authors and reviewers. The authors should mainly focus on factual errors in the reviews and concrete questions posed by the reviewers. New research results can also be discussed if they help to clarify open questions. More instructions will be sent out to the authors at the beginning of the rebuttal period.

Best Paper Award

Outstanding paper(s) will be selected by the Program Committee, with input from the Steering Committee, for the best paper award. The award will be announced at the conference. Best paper awards are intended to highlight papers which significantly challenge the state of the art in research areas relevant to SaTML.

Attendance for authors of accepted papers

At least one author​ ​of​ ​accepted papers​ ​will​ ​present​ ​their​ ​work​ ​at​ ​the​ ​conference​ ​and​ ​their papers​ ​will​ ​appear​ ​in​ ​the​ ​conference’s​ ​formal​ ​IEEE​ ​proceedings. In the event of difficulty in obtaining visas for travel and other exceptional circumstances, exceptions may be made and will be discussed on a case-by-case basis.

If you have any questions, please email us at contact@satml.org