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 Workshop of the Machine learning Optimization and big Data (MOD 2017), and for inclusion in the proceedings companion to be published on the MOD 2017 web site.
Following the success of the last year 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.
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 MOD 2017 web site.
Please prepare your paper in English using the Lecture Notes in Computer Science (LNCS) template, which is available here. Papers must be submitted in PDF.
When submitting a paper to MOD 2017, authors are required to select one of the following four types of papers:
For research work which is relevant and which may solicit fruitful discussion at the conference.
All accepted long papers will be published in a volume of the series on Lecture Notes in Computer Science from Springer after the conference. Instructions for preparing and submitting the final versions (camera-ready papers) of all accepted papers will be available later on.
All the other papers (short papers, abstract of the oral presentations, poster presentations) will be published on the MOD 2017 web site.
All papers must be submitted using EasyChair. The link to submit papers is the following:
MOD Conference past editions: