ICML 2026 LaTeX 模板（icml2026.sty）

\documentclass{article}
\usepackage[preprint]{icml2026}
\usepackage{amsmath}

\icmltitlerunning{Variance-Reduced Extragradient}

\begin{document}
\twocolumn[
  \icmltitle{Variance-Reduced Extragradient for Smooth Min-Max Problems}
  \icmlsetsymbol{equal}{*}
  \begin{icmlauthorlist}
    \icmlauthor{Erika Mustermann}{equal,eu}
    \icmlauthor{Hans Mustermann}{equal,ies}
    \icmlauthor{John Doe}{eu}
  \end{icmlauthorlist}
  \icmlaffiliation{eu}{Department of Computer Science, Example University, Musterstadt, Germany}
  \icmlaffiliation{ies}{Institute of Example Studies, Exampleton, USA}
  \icmlcorrespondingauthor{Erika Mustermann}{erika.mustermann@example.org}
  \vskip 0.3in
]
\printAffiliationsAndNotice{\icmlEqualContribution}

\begin{abstract}
We combine extragradient steps with variance reduction and reach a duality gap of
$10^{-6}$ with fewer gradient evaluations on problems of size 44 to 4,400.
\end{abstract}

\section{Introduction}
Many training problems are saddle-point problems $\min_x \max_y f(x, y)$.
\end{document}