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<frame >
<titlepage>
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<frame title="Task">
<i f="../prep/01Task/table.jpg"></i>
</frame>

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<frame title="Intro">
<list>
<e>Motivation</e>
<l2st>
<e>AD is super important...</e>
<e>good AD = complicated models -> Many Parameters</e>
<e>Evaluation dependent on very few datapoints->Optimization impossible</e>
</l2st>
<e>Open Challenges</e>
<l2st>
<e>Evaluate without testing data</e>
<e>Formalisation of existing ideas</e>
<e>Numerical assessment of them</e>
</l2st>
<e>Contribution</e>
<l2st>
<e>Suggest new methods for AE</e>
<e>Compare methods on many datasets</e>
<e>Seperate into parameter and hyperparameter optimisation</e>
</l2st>
</list>
</frame>

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<i f="../prep/04RW/graph.png"></i>
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<frame title="Problem Statement">
<list>
<e>Given $N$ Anomaly detection methods $M_i = TrainModel(X_{train})$, find $f(M_i)$ so that Score $S_i = f(M_i)$ can be used to find an above average AD method $M_{argmax(S)}$.</e>
<e>Let $TrainMany(X_{train},C)=TrainModel(X_{train})_{argmax(f(M_0...M_C))}$. We assume the distribution of $TrainMany$ to be gaussian and describe it through $\mu_C$ and $\sigma_C$. We consider a function $f(M)$ to be helpful, if $\Delta = \frac{sqrt(N) \cdot (\mu_C-\mu_1)}{sqrt(\sigma_C^2+\sigma_1^2)} > 3$ for some number of models tested $N$.</e>
</list>
</frame>

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<plt>
<name Current experiment status>
<title Unsupervised Optimisation - Paper Structure>
<stitle u-Opt>
<institute ls9 tu Dortmund>
<theme CambridgeUS>
<colo dolphin>
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pdflatex main.tex
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pdflatex main.tex
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\UseRawInputEncoding
%\documentclass[hyperref={pdfpagelabels=false}]{beamer}
\documentclass[hyperref={pdfpagelabels=false},aspectratio=169]{beamer}
% Die Hyperref Option hyperref={pdfpagelabels=false} verhindert die Warnung:
% Package hyperref Warning: Option `pdfpagelabels' is turned off
% (hyperref) because \thepage is undefined.
% Hyperref stopped early
%
\usepackage{lmodern}
% Das Paket lmodern erspart die folgenden Warnungen:
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% Wenn \titel{\ldots} \author{\ldots} erst nach \begin{document} kommen,
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% Package hyperref Warning: Option `pdfauthor' has already been used,
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% Daher steht es hier vor \begin{document}
\title[u-Opt]{Unsupervised Optimisation - Paper Structure}
\author{Simon Kluettermann}
\date{\today}
\institute{ls9 tu Dortmund}
% Dadurch wird verhindert, dass die Navigationsleiste angezeigt wird.
\setbeamertemplate{navigation symbols}{}
% zusaetzlich ist das usepackage{beamerthemeshadow} eingebunden
\usepackage{beamerthemeshadow}
\hypersetup{pdfstartview={Fit}} % fits the presentation to the window when first displayed
\usepackage{appendixnumberbeamer}
\usepackage{listings}
\usetheme{CambridgeUS}
\usepackage{ngerman}
\usecolortheme{dolphin}
% \beamersetuncovermixins{\opaqueness<1>{25}}{\opaqueness<2$\Rightarrow${15}}
% sorgt dafuer das die Elemente die erst noch (zukuenftig) kommen
% nur schwach angedeutet erscheinen
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\begin{document}
%from file ../uopt/data/000.txt
\begin{frame}[label=]
\frametitle{}
\begin{titlepage}
\centering
{\huge\bfseries \par}
\vspace{2cm}
{\LARGE\itshape Simon Kluettermann\par}
\vspace{1.5cm}
{\scshape\Large Master Thesis in Physics\par}
\vspace{0.2cm}
{\Large submitted to the \par}
\vspace{0.2cm}
{\scshape\Large Faculty of Mathematics Computer Science and Natural Sciences \par}
\vspace{0.2cm}
{\Large \par}
\vspace{0.2cm}
{\scshape\Large RWTH Aachen University}
\vspace{1cm}
\vfill
{\scshape\Large Department of Physics\par}
\vspace{0.2cm}
{\scshape\Large Insitute for theoretical Particle Physics and Cosmology\par}
\vspace{0.2cm}
{ \Large\par}
\vspace{0.2cm}
{\Large First Referee: Prof. Dr. Michael Kraemer \par}
{\Large Second Referee: Prof. Dr. Felix Kahlhoefer}
\vfill
% Bottom of the page
{\large November 2020 \par}
\end{titlepage}
\pagenumbering{roman}
\thispagestyle{empty}
\null
\newpage
\setcounter{page}{1}
\pagenumbering{arabic}
\end{frame}
%from file ../uopt/data/001Task.txt
\begin{frame}[label=Task]
\frametitle{Task}
\begin{figure}[H]
\centering
\includegraphics[height=0.9\textheight]{../prep/01Task/table.jpg}
\label{fig:prep01Tasktablejpg}
\end{figure}
\end{frame}
%from file ../uopt/data/002Intro.txt
\begin{frame}[label=Intro]
\frametitle{Intro}
\begin{itemize}
\item Motivation
\begin{itemize}
\item AD is super important...
\item good AD = complicated models $\Rightarrow$ Many Parameters
\item Evaluation dependent on very few datapoints$\Rightarrow$Optimization impossible
\end{itemize}
\item Open Challenges
\begin{itemize}
\item Evaluate without testing data
\item Formalisation of existing ideas
\item Numerical assessment of them
\end{itemize}
\item Contribution
\begin{itemize}
\item Suggest new methods for AE
\item Compare methods on many datasets
\item Seperate into parameter and hyperparameter optimisation
\end{itemize}
\end{itemize}
\end{frame}
%from file ../uopt/data/003RW.txt
\begin{frame}[label=RW]
\frametitle{RW}
\begin{figure}[H]
\centering
\includegraphics[height=0.9\textheight]{../prep/04RW/graph.png}
\label{fig:prep04RWgraphpng}
\end{figure}
\end{frame}
%from file ../uopt/data/004RW.txt
\begin{frame}[label=RW]
\frametitle{RW}
\begin{figure}[H]
\centering
\includegraphics[height=0.9\textheight]{../prep/05RW/graph.png}
\label{fig:prep05RWgraphpng}
\end{figure}
\end{frame}
%from file ../uopt/data/005But....txt
\begin{frame}[label=But...]
\frametitle{But...}
\begin{figure}[H]
\centering
\includegraphics[height=0.9\textheight]{../prep/06But.../graph.png}
\label{fig:prep06Butgraphpng}
\end{figure}
\end{frame}
%from file ../uopt/data/006Problem Statement.txt
\begin{frame}[label=Problem Statement]
\frametitle{Problem Statement}
\begin{itemize}
\item Given $N$ Anomaly detection methods $M_i = TrainModel(X_{train})$, find $f(M_i)$ so that Score $S_i = f(M_i)$ can be used to find an above average AD method $M_{argmax(S)}$.
\item Let $TrainMany(X_{train},C)=TrainModel(X_{train})_{argmax(f(M_0...M_C))}$. We assume the distribution of $TrainMany$ to be gaussian and describe it through $\mu_C$ and $\sigma_C$. We consider a function $f(M)$ to be helpful, if $\Delta = \frac{sqrt(N) \cdot (\mu_C-\mu_1)}{sqrt(\sigma_C^2+\sigma_1^2)} > 3$ for some number of models tested $N$.
\end{itemize}
\end{frame}
\end{document}

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<titlepage>

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Motivation
<l2st>
AD is super important...
good AD = complicated models -> Many Parameters
Evaluation dependent on very few datapoints->Optimization impossible
</l2st>
Open Challenges
<l2st>
Evaluate without testing data
Formalisation of existing ideas
Numerical assessment of them
</l2st>
Contribution
<l2st>
Suggest new methods for AE
Compare methods on many datasets
Seperate into parameter and hyperparameter optimisation
</l2st>

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Given $N$ Anomaly detection methods $M_i = TrainModel(X_{train})$, find $f(M_i)$ so that Score $S_i = f(M_i)$ can be used to find an above average AD method $M_{argmax(S)}$.
Let $TrainMany(X_{train},C)=TrainModel(X_{train})_{argmax(f(M_0...M_C))}$. We assume the distribution of $TrainMany$ to be gaussian and describe it through $\mu_C$ and $\sigma_C$. We consider a function $f(M)$ to be helpful, if $\Delta = \frac{sqrt(N) \cdot (\mu_C-\mu_1)}{sqrt(\sigma_C^2+\sigma_1^2)} > 3$ for some number of models tested $N$.

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