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\pdfminorversion=5
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\newcommand{\methodname}[0]{\textsc{GPS}}

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\title{Conformal Prediction Sets for Deep Generative Models \\ via Reduction to Conformal Regression}

% The standard author block has changed for UAI 2025 to provide
% more space for long author lists and allow for complex affiliations
%
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% Add authors
\author{Hooman Shahrokhi$^*$}
\author{Devjeet Roy\thanks{These authors contributed equally to the work.}}
\author{Yan Yan}
\author{Venera Arnaoudova}
\author{Jana Doppa}
% Add affiliations after the authors
\affil{%
    School of EECS \\
    Washington State University \\
    Pullman, WA, USA
}
  
  \begin{document}
\maketitle
\begin{abstract}
 We consider the problem of generating valid and small prediction sets by sampling outputs (e.g., software code and natural language text) from a black-box deep generative model for a given input (e.g., textual prompt). 
The validity of a prediction set is determined by a user-defined binary admissibility function depending on the target application. For example, requiring at least one program in the set to pass all test cases in code generation application.
To address this problem, we develop a simple and effective conformal inference algorithm referred to as {\em Generative Prediction Sets (GPS)}. Given a set of calibration examples and black-box access to a deep generative model, GPS can generate prediction sets with provable guarantees. The key insight behind GPS is to exploit the inherent structure within the distribution over the minimum number of samples needed to obtain an admissible output to develop a simple conformal regression approach over the minimum number of samples. %Unlike prior work , the sets generated by GPS  do not require iterative sampling at test time, while maintaining strict marginal coverage guarantees. 
Experiments on multiple datasets for code and math word problems using different large language models demonstrate the efficacy of GPS over state-of-the-art methods.

\end{abstract}

\input{sections/introduction}
\input{sections/background}
\input{sections/method}
\input{sections/experiments}
\input{sections/related_work}

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\newpage

\onecolumn

\title{Appendix}
\maketitle



This Supplementary Material should be submitted together with the main paper.

\appendix
\input{sections/appendix}
%% APPENDIX GOES HERE

\end{document}
