This inaugural IMS-sponsored workshop aims to ignite conversations and collaborations at the intersection of statistics and machine learning. Featuring peer-reviewed, open-call submissions for four-page extended abstracts, this event will showcase ideas in statistical machine learning that deserve broader attention within the statistical community.

The Inaugural Workshop on
Frontiers in Statistical Machine Learning (FSML)

Background

The Institute of Mathematical Statistics (IMS) is proud to introduce the annual IMS Frontiers in Statistical Machine Learning (FSML) workshop series. This series is dedicated to exploring emerging and impactful topics in the field of statistical machine learning that have yet to receive significant attention in leading IMS and ASA publications. Each year, the FSML workshop will spotlight 2-3 themes where research is rapidly evolving, encouraging the dissemination of novel ideas and fostering deeper engagement within the community.

Inspired by the dynamic format of machine learning conference workshops, FSML brings a fresh and interactive approach to the statistics landscape. The workshop will host an open call for short paper submissions, followed by a rigorous and transparent review process to ensure the highest quality of contributions. This inclusive model is designed to promote the exchange of innovative research, stimulating conversation and collaboration among attendees.

Accepted submissions will be presented through poster sessions and enriched by panel discussions that provide real-time feedback and networking opportunities. This structure is aimed at enhancing dialogue between researchers and practitioners, paving the way for future advances in the field.

Stay tuned for our call for papers and join us in shaping the future of statistical machine learning!

Conference Venue

The inaugural FSML workshop will take place on August 2, 2025, at Vanderbilt University in Nashville, Tennessee, just ahead of the 2025 Joint Statistical Meetings (JSM).

Topics

There will be two main streams in the 2025 workshop:

1) The Science of Deep Learning

2) Statistical Learning from Heterogeneous Data Sources and Generalization

Submission Details

Double-Blind Reviewing

The review process for the workshop will be double-blind, i.e. reviewers will not know the authors' identity (and vice versa). Authors should ensure their anonymity in the submitted papers. In brief:

authors' names should not be included in the submitted pdf;
please refer to your prior work in the third person wherever possible;
a reviewer may be able to deduce the authors' identities by using external resources, such as technical reports published on the web. The availability of information on the web that may allow reviewers to infer the authors' identities does not constitute a breach of the double-blind submission policy.

Note that anonymizing your paper is mandatory, and papers that explicitly or implicitly reveal the authors' identities may be rejected.

Paper Submission

Please read the following paper submission guidelines before submitting your papers:

Each paper should not reveal author's identities (double-blind review process).
Papers will be checked for plagiarism. The Program Committee reserves the right to desk-reject a paper if it contains elements that are suspected to be plagiarized.
Submissions can be existing work or be currently under review at another venue.
Each paper is limited to 4 pages, including figures, tables, and references.
New authors cannot be added at the time of submitting final camera-ready papers.
If you encounter any problems with the submission of your papers, please contact the conference submission chair.