Workshop Track
Extended abstracts of 3–5 pages (excluding references/appendices); subject to double-blind peer review; for new research ideas or work-in-progress; presented as posters.
Machine Learning/CORE Unranked
FSML 2026
The Second Workshop on Frontiers in Statistical Machine Learning
FSML 2026 is an annual workshop co-located with ICSDS 2026 in Split, Croatia, organized by the Institute of Mathematical Statistics (IMS) to highlight emerging topics in statistical machine learning that have not yet received significant attention in major statistical publications. The workshop features two core themes—Generative and Foundation Models for Statistics and Science of Deep Learning—and offers two submission tracks: a Workshop Track for new research and a Fast Track for recently accepted papers from top ML venues.
Key deadlines
Verified August 30, 2026Full paper
October 1, 2026
AoE
Workshop timeline
Submission and decisions
Full paperKey deadline
October 1, 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 accepted at major ML venues (NeurIPS 2025, ICLR 2026, AISTATS 2026, ICML 2026, UAI 2026, JMLR, TMLR) since August 2025; no additional review; author names not anonymized; presented as posters.
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 3–5 pages (excluding references/appendices).
- Workshop Track uses double-blind peer review.
- Papers currently under review elsewhere may be submitted to the Workshop Track without extension, but must still meet page limits.
- Previously published work must be substantially extended to be considered for the Workshop Track.
- Fast Track submissions require the original camera-ready PDF with author names included.
- Fast Track submissions are not peer-reviewed; selection is based on eligibility and fit with core themes.
- Dual submission to other venues is allowed for Workshop Track if the paper is under review elsewhere, but not for previously published work without substantial extension.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified August 30, 2026