43 lines
1.7 KiB
TeX
43 lines
1.7 KiB
TeX
\begin{frame}
|
||
\frametitle{Topic 1: Shapley Values}
|
||
|
||
%\Large \\
|
||
\LARGE \textbf{Chapter:} 9.2,9.5 and 9.6 + 2/3 papers \\
|
||
\textbf{Supervisor:} Chiara Balestra (chiara.balestra@cs.uni-dortmund.de)\\
|
||
|
||
%\begin{center}
|
||
%\includegraphics[height=3cm]{illustrations/chiara.png}
|
||
%\end{center}
|
||
|
||
\begin{columns}
|
||
\begin{column}{.575\textwidth}
|
||
\begin{center}
|
||
\includegraphics[height=3cm]{illustrations/chiara.png}
|
||
\end{center}
|
||
\end{column}
|
||
\begin{column}{.375\textwidth}
|
||
\begin{itemize}
|
||
\item Use game theory to explain the output of a model
|
||
\begin{enumerate}
|
||
\item on cs theory
|
||
\begin{itemize}
|
||
\item \textbf{Shapley Values for Feature Selection: The Good, the Bad, and the Axioms} (Fryer et al. 2020)
|
||
\item \textbf{Explaining Models by Propagating Shapley Values of Local Components} (Chen et al. 2020)
|
||
\item \textbf{GraphSVX: Shapley Value Explanations for Graph Neural Networks} (Duval et al. 2021)
|
||
\end{itemize}
|
||
\item on medical application
|
||
\begin{itemize}
|
||
\item \textbf{Identifying mortality factors from Machine Learning using Shapley values – a case of COVID19} (Smith et al. 2021)
|
||
\item \textbf{Explaining multivariate molecular diagnostic tests via Shapley values} (Roder et al. 2021)
|
||
\end{itemize}
|
||
\end{enumerate}
|
||
|
||
\end{itemize}
|
||
|
||
\end{column}
|
||
\end{columns}
|
||
|
||
|
||
|
||
\end{frame}
|