diff --git a/beamerthemePoster.sty b/beamerthemePoster.sty index ceaaf3f..067e5c4 100644 --- a/beamerthemePoster.sty +++ b/beamerthemePoster.sty @@ -173,7 +173,7 @@ % \shade [inner color=color2,outer color=color3] (0,0) rectangle (\columnwidth,0.3cm); % \end{tikzpicture} % \end{flushleft} - \vspace{5pt} + % \vspace{5pt} % \begin{center} %\vskip1cm @@ -187,7 +187,7 @@ % \shade [inner color=color2,outer color=color3] (0,0) rectangle (\columnwidth,0.3cm); % \end{tikzpicture} % \end{flushleft} - \vspace{5pt} + % \vspace{2.5pt} % {\parskip0pt\par} \justifying diff --git a/beamerthemeSlides.sty b/beamerthemeSlides.sty index 2344484..7499191 100644 --- a/beamerthemeSlides.sty +++ b/beamerthemeSlides.sty @@ -109,7 +109,7 @@ \else% \hskip12pt% \fi% - \insertsubsectionhead\hfill\insertshortauthor\hskip12pt\insertframenumber/\inserttotalframenumber\hspace{0.5em} + \insertsubsectionhead\hfill\insertshortauthor\hskip6pt\insertshortinstitute\hskip12pt\insertframenumber/\inserttotalframenumber\hspace{0.5em} \end{beamercolorbox} \begin{beamercolorbox}[wd=\paperwidth,colsep=1.5pt]{lower separation line head} \end{beamercolorbox} @@ -123,7 +123,7 @@ % item settings \setbeamertemplate{itemize item}{$\color{beamer@simple@color}\bullet$} \setbeamertemplate{itemize subitem}{$\color{beamer@simple@color}\bullet$} -\setbeamertemplate{enumerate items}[square] +\setbeamertemplate{enumerate items}[default] \setbeamertemplate{section in toc}[sections numbered] \setbeamertemplate{subsection in toc}[square] diff --git a/poster.tex b/poster.tex index 300d954..25ca84a 100644 --- a/poster.tex +++ b/poster.tex @@ -23,7 +23,7 @@ %==Title, date and authors of the poster======================================= \title -[IJCAI25, 29 - 31 August 2025, Guangzhou, China] % Conference +[34th International Joint Conference on Artificial Intelligence (IJCAI25), 29 - 31 August 2025, Guangzhou, China] % Conference { % Poster title Large-Scale Trade-Off Curve Computation for Incentive Allocation with Cardinality and Matroid Constraints } @@ -36,6 +36,10 @@ Large-Scale Trade-Off Curve Computation for Incentive Allocation with Cardinalit \begin{document} + +% larger font +\Large + \begin{frame}[t] %============================================================================== \begin{multicols}{2} @@ -45,78 +49,7 @@ Large-Scale Trade-Off Curve Computation for Incentive Allocation with Cardinalit \section{Introduction} -\lipsum - -\begin{equation} -H = \sum_{i=1}^{N} h_{D}(i) + \sum_{j>i=1}^{N} C_{ij} -\end{equation} - -In Ref.~\cite{ref1}... -In Refs.~\cite{ref1,ref2}... -On webpage~\cite{web}... - - -\section{Result and discussions} - - -\vskip1ex -\begin{table} -\centering -\caption{This is a table with scientific results.} -\begin{tabular}{ccccc} -\hline\hline -1 & 2 & 3 & 4 & 5\\ -\hline -aaa & bbb & ccc & ddd & eee\\ -aaaa & bbbb & cccc & dddd & eeee\\ -aaaaa & bbbbb & ccccc & ddddd & eeeee\\ -aaaaaa & bbbbbb & cccccc & dddddd & eeeeee\\ -1.000 & 2.000 & 3.000 & 4.000 & 5.000\\ -\hline\hline -\end{tabular} -\end{table} -\vskip2ex - -blabla - -\vskip1ex -\begin{figure} -\centering -\includegraphics[width=0.99\columnwidth]{./image/landscape.png} -\caption{This is a picture with scientific results.} -\end{figure} -\vskip2ex - -sdfsdf - - -\subsection{SubSection, a very very very very very very long title} - -sdfsdf - -\section{Summary and conclusions} - -\lipsum[1-3] - - -%============================================================================== -%==End of content============================================================== -%============================================================================== - -%--References------------------------------------------------------------------ - -\subsection{References} - -\begin{thebibliography}{99} - -\bibitem{ref1} J.~Doe, Article name, \textit{Phys. Rev. Lett.} - -\bibitem{ref2} J.~Doe, J. Smith, Other article name, \textit{Phys. Rev. Lett.} - -\bibitem{web} \url{http://www.google.pl} - -\end{thebibliography} -%--End of references----------------------------------------------------------- +\lipsum[1-7] \end{multicols} diff --git a/slides.tex b/slides.tex index ecb152c..b85c5c7 100644 --- a/slides.tex +++ b/slides.tex @@ -3,9 +3,10 @@ \usetheme{Slides} \usepackage{algo} \usepackage{soul} +% \usepackage{cancel} \title[Incentive allocation]{Large-Scale Trade-Off Curve Computation for Incentive Allocation with Cardinality and Matroid Constraints} -\date{\today} +\date{August 30, 2025} \author{\underline{Yu Cong}, Chao Xu, Yi Zhou} \institute[UESTC]{University of Electronic Science and Technology of China} % \AtBeginSection[]{ @@ -17,9 +18,76 @@ \begin{document} \begin{frame}[plain] \titlepage + \scriptsize 34th International Joint Conference on Artificial Intelligence (IJCAI25) \end{frame} -\begin{frame}{title ggg fff} - \ul{sdfggg} +% introduce the problem. 3 things: trade-off curve, approximation, general constraints + +\begin{frame}{Incentive allocation with constraints} +A ride sharing company wants to send riders promotional coupons in the hope of more rides. + +% each agent gets at most 1 coupon. +\begin{figure} +placeholder\\ +\includegraphics[width=.5\textwidth]{image/landscape.png} +\end{figure} + +\end{frame} + +\begin{frame}{Multiple-choice knapsack} +\textbf{Input}: $n$ sets of coupons $K_1,\dots,K_n$. Each coupon $e\in K_i$ has a non-negative cost $c_e\in \Z_+$ and value $v_e\in \Z_+$. A positive budget $b\in \Z_+$. + +\textbf{Output}: A (multi)set of coupons $K$ that maximizes the total value $\sum_{e\in K} c_e$ while satisfying \textcolor{Red}{$|K\cap K_i|\leq 1$} and $\sum_{e\in K} c_e\leq b$. + +\vspace{1em} +\pause + +Three problems with this modeling: +\begin{enumerate} +\item Finding the exact optimum is NP-hard. So we consider solving it approximately. +\item Companies may run multiple campaigns at the same time. So a trade-off curve between budget and profit will be useful. +\item The multiple-choice constraint \textcolor{Red}{$|K\cap K_i|\leq 1$} is too weak for real applications. +\end{enumerate} + +\end{frame} + +\begin{frame}{Linear programming formulation} +\textcolor{gray}{ +\textbf{Input}: $n$ sets of coupons $K_1,\dots,K_n$. Each coupon $e\in K_i$ has a non-negative cost $c_e\in \Z_+$ and value $v_e\in \Z_+$. \st{A positive budget $b\in \Z_+$.} +} +\pause +\begin{equation*} +\begin{aligned} +\tau(b)= \max_x& & v\cdot x& & &\\ +s.t.& & c\cdot x&\leq b & &\\ +& & \textcolor{Plum}{x_{K_i}}&\textcolor{Plum}{\in P_{K_i}} & &\forall i\in [n]\\ +\end{aligned} +\end{equation*} + +\textbf{Output}: A compact representation of $\tau(b)$. +\pause + +We focus on 2 kinds of constraints of \textcolor{Plum}{$x_{K_i}\in P_{K_i}$}. +\begin{enumerate} +\item Cardinality. \textcolor{Plum}{$x_{K_i}\in P_{K_i}$}→ $\sum_{e\in K_i}x_e\leq p$. +\item Matroid. \textcolor{Plum}{$x_{K_i}\in P_{K_i}$}→ $x_{K_i}$ is in the base polytope of a matroid $M_i$. +\end{enumerate} +\end{frame} + +\begin{frame}{Results} +We compute the curve $\tau(b)$ fast. +\begin{theorem} +Consider an incentive allocation problem with a total of $m$ incentives. +The trade-off curve is piecewise linear concave function with $k$ breakpoints. +\begin{itemize} +\item Cardinality constraint. +$k=O(mp^{1/3})$ and $\tau$ can be computed in $O((k+m)\log m)$ time. +\item Matroid constraint. $k=O(mr^{1/3})$ and $\tau$ can be computed in $O(Tk+k\log m)$ time. +\end{itemize} +\end{theorem} +\end{frame} + +\begin{frame}[plain] +poster \end{frame} \end{document} \ No newline at end of file