From dfbfe94134fcd4583586ce63a241b223112d3403 Mon Sep 17 00:00:00 2001 From: Alexander Munch-Hansen Date: Mon, 4 Nov 2019 12:36:01 +0100 Subject: [PATCH] Init --- graphics/.DS_Store | Bin 0 -> 6148 bytes graphics/christ_vs_atheism.png | Bin 0 -> 825188 bytes graphics/christ_vs_atheism_annotated_1.png | Bin 0 -> 858466 bytes graphics/christ_vs_atheism_annotated_2.png | Bin 0 -> 865108 bytes graphics/doctor_pred.png | Bin 0 -> 234940 bytes pres.tex | 206 +++++++++++++++++++++ 6 files changed, 206 insertions(+) create mode 100644 graphics/.DS_Store create mode 100644 graphics/christ_vs_atheism.png create mode 100644 graphics/christ_vs_atheism_annotated_1.png create mode 100644 graphics/christ_vs_atheism_annotated_2.png create mode 100644 graphics/doctor_pred.png create mode 100644 pres.tex diff --git a/graphics/.DS_Store b/graphics/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 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t{OA64X#W4r{rAQEUk}^==Qj5zinQWss~bLj$~pM=R8;11q40}W{}<+zjuZd@ literal 0 HcmV?d00001 diff --git a/pres.tex b/pres.tex new file mode 100644 index 0000000..b094a20 --- /dev/null +++ b/pres.tex @@ -0,0 +1,206 @@ +\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}

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