ecml-pkdd

name	ecml-pkdd
description	Writing and formatting guidance for ECML PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) papers. Use when drafting, formatting, or submitting to ECML PKDD 2026. Covers Springer LNCS llncs class, page limits, natbib citation commands, ML-specific writing conventions, figure/table formatting, and camera-ready submission requirements.

ECML PKDD 2026 Conference Writing Skill

CCF-B | ML/DB | Publisher: Springer Conference: https://ecmlpkdd.org/2026 Template: ECML-PKDD/llncs.cls (Springer LNCS)

Document Setup

Preamble Structure

\documentclass[envcountsame]{llncs}

\usepackage{makeidx}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{amsthm}
\usepackage{graphicx}
\usepackage{url}
\usepackage{natbib}
\usepackage{hyperref}
\hypersetup{colorlinks=true}

Springer LNCS Options

Option	Description
envcountsame	Number theorem-like environments per section
envcountreset	Reset theorem counters per section
runningheads	Short titles in page headers

Page Limits

|| Section | Limit | |---------|-------| | Main paper | 14 pages | | References | No specific limit |

ECML PKDD typically allows 14 pages for the main content. Check the official CFP for the current year's exact limits, as limits may differ between research papers and applied data science papers.

Title and Author Formatting

\title{Your Paper Title Here}

\author{First Author\inst{1} \and
        Second Author\inst{2}}

\institute{
  Institution 1\\
  \email{[e-mail]}
  \and
  Institution 2\\
  \email{[e-mail]}
}

Abstract

\begin{document}
\maketitle

\begin{abstract}
Your abstract here. Summarize the problem, approach, and main results.
\keywords{keyword1, keyword2, keyword3}
\end{abstract}

Section Organization

Standard ECML PKDD paper structure:

1. Abstract — Summary of problem, approach, and results
2. Introduction — Problem, motivation, contributions (enumerate)
3. Background and Notation — ML background and definitions
4. Proposed Method — Core contribution with formal treatment
5. Theoretical Analysis (optional) — Theoretical guarantees
6. Experimental Evaluation — Datasets, baselines, results, analysis
7. Related Work — Comparison with prior ML/KDD work
8. Conclusion — Summary and future directions
9. References

Figures and Tables

\begin{figure}[t]
\centering
includegraphics[width=0.8\linewidth]{fig/model}
\caption{Proposed model architecture.}
\label{fig:model}
\end{figure}

• Use PDF, PNG, or EPS
• Ensure grayscale legibility
• Self-contained captions
• Number figures and tables sequentially
• Use booktabs for tables (no vertical rules)

References (natbib)

\bibliographystyle{spbasic}  % Springer basic style
\bibliography{references}

\citet{smith2023}      % Smith et al. (2023)
\citep{smith2023}      % (Smith et al. 2023)

ML-Specific Writing Conventions

• Clearly state research questions and hypotheses
• Report standard ML metrics (accuracy, F1, AUC, RMSE, etc.)
• Include confidence intervals or statistical significance tests
• Describe datasets: source, size, preprocessing, train/test split
• Include ablation studies when applicable
• Address reproducibility (code, hyperparameters, random seeds)

Formatting Rules

• Format: Two-column, single-spaced
• Paper size: A4
• Body font: 10pt
• Margins: 2.5cm all around

Submission Checklist

• 14 pages or fewer (main body)
• Springer LNCS llncs document class
• Abstract and keywords included
• All figures in .pdf/.png/.eps format
• References in correct format
• Compiles with pdflatex
• ML metrics reported with statistical significance