Call for Abstracts

Abstracts submission

All papers must be submitted using EasyChair.

Abstract Submission deadline: Monday April 10, 2023 (AoE)

Author agreement: by submitting an abstract, the author(s) agree that, if their abstract/poster/oral-presentation is accepted, they will:

  • register at least one author to attend the conference (by Monday April 10, 2023 (AoE));
  • attend the conference (at least one author) and present the accepted abstract at the conference.

Any questions regarding the submission process can be sent to conference organizers: lod@icas.cc.

Two-page abstracts describing late-breaking developments in the field of Machine Learning, Optimization and Data Science are solicited for presentation at the Late-Breaking Abstracts Conference on Machine Learning, Optimization, and Data Science (LOD 2023) and for inclusion in the proceedings companion to be published on the LOD 2023 website.

Submitted abstracts must adopt the following to two rules:

  • author names and institutions must be omitted, and
  • references to authors’ own related research work must be in the third person.

Presentation Format

Following the success of last year’s poster format for Late-Breaking Abstracts, authors of the accepted submissions will be asked to prepare a poster summarizing their contributions. The chair will introduce each work at the beginning of the session and attendees will have the opportunity to interact with authors and enjoy a dynamic forum to share and spread scientific ideas. The details about the poster preparation will be sent to the authors of accepted abstracts.

Selection Process

Late-breaking abstracts will be briefly examined for relevance and minimum standards of acceptability but will not be peer-reviewed in detail. Authors of accepted late-breaking abstracts will individually retain copyright (and all other rights) to their late-breaking abstracts. Accepted late-breaking abstracts with no author registered by the deadline will not appear in the Late-Breaking Abstracts section on the LOD 2023 website.