Workshop Track
Extended abstracts of 3–5 pages (excluding references/appendices) for new research ideas or work-in-progress; subject to double-blind peer review.
Machine Learning/CORE Unranked
FSML 2026
The Second Workshop on Frontiers in Statistical Machine Learning
The Frontiers in Statistical Machine Learning (FSML) 2026 workshop, co-located with ICSDS 2026 in Split, Croatia, is an annual event organized by the Institute of Mathematical Statistics to highlight emerging topics in statistical machine learning that have not yet received significant attention in major statistical publications. It features two core themes focused on generative/foundation models and the science of deep learning, with a dual submission track for both new research and recently accepted ML papers.
Key deadlines
Verified September 9, 2026Full paper
October 20, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
October 20, 2026 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Fast Track (Recent Publications)
Original camera-ready PDFs of papers recently accepted at major ML venues (e.g., NeurIPS, ICML, JMLR, TMLR) since August 2025; no additional review, author names not anonymized.
Research areas in scope
01
Area 1: Generative and Foundation Models for Statistics
Deep Generative Modeling: Diffusion models, normalizing flows, and other generative approaches for density estimation, sampling, and synthetic dataFoundation Models for Tabular Data: Pretrained and in-context models for prediction and inference on structured dataLLMs for Statistical Reasoning: Large language models as tools for data analysis, hypothesis generation, and automated workflowsAmortized and Simulation-Based Inference: In-context learning and pretrained networks for Bayesian and likelihood-free inference
02
Area 2: Science of Deep Learning
Theoretical Foundations: Exploring mathematical and statistical principles underlying deep learningPhenomenological Studies of Learning Systems: Cataloging and explaining intriguing behaviors in learning dynamicsInterpretability, Alignment, and Safety: Understanding and guiding AI systems to ensure ethical and safe operationEmerging Learning Paradigms: Investigating new approaches, such as in-context learning and scaling laws
Policies worth checking twice
- Submissions are non-archival and do not have proceedings.
- Workshop Track submissions must be double-blind; Fast Track submissions do not require anonymization.
- Previously published work must be substantially extended to be considered for the Workshop Track.
- Papers currently under review elsewhere may be submitted to the Workshop Track without extension, provided they meet page limits.
- Fast Track submissions are eligible only if accepted at specified venues (NeurIPS 2025, ICLR 2026, AISTATS 2026, ICML 2026, UAI 2026, JMLR, TMLR) since August 2025.
- Dual submission to other venues is permitted for Workshop Track as long as the paper is not simultaneously published.
- Travel award applicants must be first authors of Workshop Track submissions and must present in person.
- Travel award recipients cannot have received the IMS Hannan, IMS New Researcher, or ICSDS student/junior researcher awards in the same year.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026