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+\documentclass{beamer}
+\usetheme[progressbar=frametitle]{metropolis}
+
+% g \in G is explanation as a model
+% f is the model we're trying to explain
+% does, being model agnostic, means we do not care about specifics of f.
+% We use Locally Weighted Square Loss as L, where I suspect pi is the weight and we thus estimate the difference between the actual model
+% and our explanation, and multiply this with the proximity of the data point z, to x.
+% Spørg lige Lasse hvorfor min(L(f,g,pi_x(z)) + omega(g)) bliver intractable, når omega(g) er en konstant!
+
+
+
+
+\usepackage{setspace}
+\usepackage[T1]{fontenc}
+\usepackage[sfdefault,scaled=.85]{FiraSans}
+%\usepackage{newtxsf}
+\usepackage[ruled, linesnumbered]{algorithm2e}
+\SetKwInput{kwRequire}{Require}
+\SetKw{kwExpl}{explain}
+
+\title{Why Should I Trust You?}
+\subtitle{Explaining the Predictions of Any Classifier}
+\author{Casper Vestergaard Kristensen \and Alexander Munch-Hansen}
+\institute{Aarhus University}
+\date{\today}
+
+\begin{document}
+\begin{frame}
+\titlepage
+\end{frame}
+
+\begin{frame}
+  \setbeamertemplate{section in toc}[sections numbered]
+  \frametitle{Outline}
+  \setstretch{0.5}
+  \tableofcontents
+
+\end{frame}
+
+
+
+
+
+\section{Meta information}
+%\subsection{Authors}
+ 
+\begin{frame}
+\frametitle{Authors}
+\begin{itemize}
+\item Marco Tulio Ribeiro
+\item Sameer Singh
+\item Carlos Guestrin
+\end{itemize}
+\end{frame}
+
+%\subsection{Publishing}
+
+\begin{frame}[fragile]{Metropolis}
+  \frametitle{Publishing}
+  
+  \begin{itemize}
+  \item Conference Paper, Research
+  \item KDD '16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
+    \begin{itemize}
+    \item A premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.
+    \item Sigkdd has the highest h5 index of any conference involving databases or data in general
+    \item Highly trusted source
+    \end{itemize}
+  \end{itemize}
+\end{frame}
+
+  \section{Article}
+
+  %\subsection{Problem}
+
+  \begin{frame}
+    \frametitle{Problem definition}
+    \begin{itemize}
+    \item People often use Machine Learning models for predictions
+    \item Blindly trusting a prediction can lead to poor decision making
+    \item We seek to understand the reasons behind predictions
+      \begin{itemize}
+      \item As well as the model doing the predictions
+      \end{itemize}
+    \end{itemize}
+  \end{frame}
+
+  %\subsection{Previous Solutions}
+  \begin{frame}
+    \frametitle{Previous Solutions}
+    \begin{itemize}
+    \item Relying on accuracy based on validation set
+    \end{itemize}
+  \end{frame}
+
+
+  \begin{frame}
+    \frametitle{A look into two predictions}
+    \includegraphics[scale=0.25]{graphics/christ_vs_atheism.png}
+  \end{frame}
+
+  \begin{frame}
+    \frametitle{A look into two predictions}
+    \includegraphics[scale=0.25]{graphics/christ_vs_atheism_annotated_1.png}
+  \end{frame}
+  
+  \begin{frame}
+    \frametitle{A look into two predictions}
+    \includegraphics[scale=0.25]{graphics/christ_vs_atheism_annotated_2.png}
+  \end{frame}
+
+  \subsection{The LIME framework}
+  \begin{frame}
+    \frametitle{LIME}
+    \begin{itemize}
+    \item The algorithm created
+    \item Explains the predictions of \emph{any} classifier or regressor in a faithful way, by approximating it locally with an \emph{interpretable} model.
+    \end{itemize}
+  \end{frame}
+
+  \begin{frame}
+    \frametitle{Intepretability}
+  \end{frame}
+  
+  \begin{frame}
+    \frametitle{Fidelity}
+  \end{frame}
+
+
+  \subsection{Explaining Predictions}
+ % \subsubsection{Examples}
+  \begin{frame}
+    % \frametitle{Sparse Linear Explanations}
+    \frametitle{Explaining an individual prediction}
+    \begin{algorithm}[H]
+      \setstretch{0.9}
+\SetAlgoLined
+\kwRequire{Classifier $f$, Number of samples $N$}
+\kwRequire{Instance $x$, and its intepretable version $x^{\prime}$}
+\kwRequire{Similarity kernel $\pi_x$, Length of explanation $K$}
+\Indp
+ $\mathcal{Z} \leftarrow \{\}$ \\
+ \For{$i \in \{1,2,3,\dots, N\}$}{
+   $z_i^{\prime} \leftarrow sample\_around(x^{\prime})$ \\
+   $\mathcal{Z} \leftarrow \mathcal{Z} \cup  \langle z_i^{\prime}, f(z_i), \pi_{x}(z_i) \rangle$ \\
+ }
+ $w \leftarrow \text{K-Lasso}(\mathcal{Z},K) \vartriangleright \text{with } z_i^{\prime} \text{ as features, } f(z) \text{ as target}$ \\
+\Return $w$
+ \caption{Sparse Linear Explanations using LIME}
+\end{algorithm}
+% This algorithm approximates the minimization problem of computing a single individual explanation of a prediction.
+% K-Lasso is the procedure of learning the weights via least squares. Wtf are these weights???
+  \end{frame}
+
+
+  \begin{frame}
+    \frametitle{Text Classification}
+  \end{frame}
+
+  \begin{frame}
+    \frametitle{Deep Networks for Images}
+  \end{frame}
+  \subsection{Explaining Models}
+  
+  \begin{frame}
+    \frametitle{Submodular Picks}
+        \begin{algorithm}[H]
+      \setstretch{0.9}
+\SetAlgoLined
+\kwRequire{Instances $X$, Budget $B$}
+\Indp
+ \ForAll{$x_i \in X$}{
+   $W_i \leftarrow \mathbf{explain}(x_i, x_i^{\prime})$ \qquad \qquad $\vartriangleright$ Using Algorithm 1
+ }
+ \For{$j \in \{1\dots d^{\prime}$} {
+   $I_j \leftarrow \sqrt{\sum_{i=1}^n |W_{ij}|}$ \qquad \qquad \quad $\vartriangleright$ Compute feature importances
+ }
+ $V \leftarrow \{\}$ \\
+ \While(\qquad \qquad \qquad \quad \ \ $\vartriangleright$ Greedy optimisation of Eq 4){$|V| < B$} {
+   $V \leftarrow V \cup  \text{argmax}_i \ c(V \cup \{i\}, W, i)$
+   }
+\Return $V$
+ \caption{Submodular pick (SP) algorithm}
+\end{algorithm}
+  \end{frame}
+
+  \section{Experiments}
+  %\subsection{Simulated User Experiments}
+  %\subsubsection{Setup}
+  \begin{frame}
+    \frametitle{Setup}
+  \end{frame}
+  %\subsection{Human Subjects}
+
+  \section{Conclusion}
+  
+  \begin{frame}
+    \frametitle{Conclusion}
+  \end{frame}
+
+  \section{Recap}
+  \begin{frame}
+    \frametitle{Recap}
+  \end{frame}
+\end{document}