• Negar Rostamzadeh
  • July 7, 2017

The 12th WiML Workshop is co-located with NIPS in Long Beach, California on Monday, December 4th and Thursday, December 7th, 2017.

The workshop is a two-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, and research scientists for an opportunity to connect and exchange ideas. There will be a panel discussion and a mentoring session to discuss current research trends and career choices in machine learning. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. While all presenters will identify primarily as female, all genders are invited to attend. This is a technical workshop with exciting technical talks.

Important Dates

  • September 12th, 2017 11:59 pm PST – Extended Abstract submission deadline
  • September 8th, 2017 11:59 pm PST – Abstract submission deadline
  • October 16th, 2017 – Notification of abstract acceptance
  • November 1st, 2017 – Travel grant application deadline
  • November 14th, 2017 – Registration Deadline
  • December 4th, 2017 – Workshop Day 1
  • December 7th, 2017 – Workshop Day 2

Submission Instructions

We strongly encourage primarily female-identifying students, post-docs and researchers in all areas of machine learning to submit an abstract describing new, previously, or concurrently published research. We welcome abstract submissions, in theory, methodology, as well as applications. Abstracts may describe completed research or work-in-progress. While the presenting author need not be the first author of the work, we encourage authors to highlight the contribution of female authors — particularly the presenting author — in the abstract.

Authors of accepted abstracts will be asked to present their work in a poster session. A few authors will be selected to give 15 minute oral presentations.  Submissions will be peer-reviewed in a double-blind setting.  Authors are encouraged to sign up to review for WiML, with a sign-up option available upon submission.  Student and post-doc authors who review for WiML will be eligible for travel awards.

Submission page:  https://cmt3.research.microsoft.com/WiML2017

Style guidelines:

  • Abstracts must not include identifying information
  • Abstracts must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be 11 points in size.  
  • Do not include any supplementary files with your submission.

Content guidelines:

  • Your abstract should stand alone, without linking to a longer paper or supplement.
  • You should convey motivation and give some technical details of the approach used.
  • While we appreciate that space is limited, some experimental results are likely to improve reviewers’ opinions of your paper.

Acceptance criteria:

All accepted papers must be presented by a primarily female-identifying author. Abstracts will be reviewed by at least two reviewers plus an area chair, who will use the following criteria:

  • Is this paper appropriate for WiML? I.e. Does it describe original research in Machine Learning or related fields?
  • Does the abstract describe work that is novel and/or an interesting application?
  • Does the abstract adequately convey the material that will be presented?

Examples of accepted abstracts from previous years. Due to the volume of submissions anticipated, we are unable to review any submitted materials besides the requested abstract.

Travel Scholarships

Registration is free. Travel Awards are available for presenting authors only.  To qualify, the author must be a student or post-doc, their abstract must be accepted, and they must volunteer to serve as a reviewer for WiML.  The amount of the travel award varies by the author’s geographical location and the total amount of funding WiML receives from our sponsors. In the past awards ranging from $300-$900 have been granted.  


  • Negar Rostamzadeh (Element AI)
  • Ehi Nosakhare (MIT)
  • Danielle Belgrave (Imperial College London)
  • Genna Gliner (Princeton University)
  • Maja Rudolph (Columbia University)


Questions? Check out the FAQs or contact us.

Copyright © Women in Machine Learning 2017