more poster

1 files changed, 16 insertions, 81 deletions

diff --git a/poster/Finale_poster/poster.tex b/poster/Finale_poster/poster.tex
index 63219a7..5da0c80 100644
--- a/poster/Finale_poster/poster.tex
+++ b/poster/Finale_poster/poster.tex
@@ -1,4 +1,4 @@
-\documentclass[final, 10]{beamer}
+\documentclass[final, 12]{beamer}
 \usepackage[utf8]{inputenc}
 \usepackage[scale=1.6]{beamerposter} % Use the beamerposter package for laying
 \usetheme{confposter} % Use the confposter theme supplied with this template
@@ -101,9 +101,13 @@
   \centering
   \includegraphics[scale=3]{graphical.pdf}
 \end{figure}
+\begin{itemize}
+  \item $\phi$: fixed source distribution
+  \item capture uncertainty on each edge
+  \item encode expressive graph priors (tied parameters)
+\end{itemize}
 \end{block}
 
-
 \end{column} % End of the first column
 
 %-----------------------------------------------------------------------------
@@ -111,91 +115,22 @@
 %-----------------------------------------------------------------------------
 \begin{column}{\onecolwid} % The first column
 
+\begin{block}{Active Learning}
+  \emph{Can we gain by choosing the source node? If so, how to best choose the
+  source node?}
 
-\end{column}
-%-----------------------------------------------------------------------------
-\begin{column}{\sepwid}\end{column}
-%-----------------------------------------------------------------------------
-
-\begin{column}{\onecolwid}
-
-\begin{block}{Sparse Recovery}
-
-\begin{itemize}
-  \item Solving for $A x = b$ when $A$ is non-degenerate is possible if
-      \begin{itemize}
-        \item $A$ is {\bf almost invertible}
-        \item $x$ is {\bf sparse}
-      \end{itemize}
-  \item If $x$ is solution to $\min L(x)$ where
-    $L$ is convex, then~\cite{Negahban:2009}~solve for:
-    \begin{equation*}
-      \min_x L(x) + \lambda \| x\|
-    \end{equation*}
-\end{itemize}
-\end{block}
-
-\begin{theorem}
-    {\bf Assumptions}:
-    \begin{itemize}
-        \item $f$ and $1-f$ are log-concave with log-gradient bounded by
-          $\frac{1}{\alpha}$
-      \item $\nabla^2 {\cal L}$ verifies the $(S,\gamma)$-{\bf
-    RE} condition
-    \vspace{1cm}
-\end{itemize} {\bf Algorithm}:
-    \begin{itemize}
-      \item Solve MLE program with $\lambda = 2\sqrt{\frac{\log m}{\alpha n}}$
-        \begin{framed}
-        \begin{equation*}
-          \hat \theta_i \in \arg \max_{\theta} {\cal L}_i(\theta_i | x^1,
-          \dots x^n) - \lambda \|\theta_i\|_1
-        \end{equation*}
-      \end{framed}
-    \end{itemize}
-    \vspace{1cm}
-    {\bf Guarantee}
-      With high probability:
-    \begin{framed}
-    \begin{equation*}
-    \|\hat \theta - \theta^*\|_2 \leq \frac{6}{\gamma} \sqrt{\frac{s \log
-      m}{\alpha n}}
-    \end{equation*}
-  \end{framed}
-    where $s$ is degree of node, $m$ is number of nodes, $n$ is the number of
-    observations
-\end{theorem}
-
-\begin{block}{Restricted Eigenvalue Condition}
-  {\bf Definition}
-  \begin{itemize}
-    \item $C(S) \equiv \{ X :\|X_{\bar S}\|_1 \leq 3 \|X\|_1\}$
-    \item Matrix $A$ verifies the $(\gamma, S)$-{(\bf RE)} condition if:
-      $$\forall X \in C({\cal S}), X^T A X \geq \gamma \|X\|_2^2$$
-  \end{itemize}
-
-   \vspace{1cm}
-  {\bf Hessian $\mapsto$ Gram Matrix}
-  \begin{itemize}
-    \item If $f$ and $1-f$ are $c$-strictly log-convex, we can replace the
-      condition on the $\nabla^2 {\cal L}$ by the same condition on the Gram
-      matrix $X^T X$.
-  \end{itemize}
-    \vspace{1cm}
-  {\bf Hessian $\mapsto$ Expected Hessian}
-  \begin{itemize}
-    \item If $\mathbb{E}[A]$ verifies the $(S, \gamma)$-{(\bf RE)} condition,
-      then $A$ verifies the $(S, \gamma/2)$-{(\bf RE)}
-     condition~\cite{vandegeer:2009}
-  \end{itemize}
+  \begin{block}{Heuristic 1 (Baseline)}
+  \end{block}
 
+  \begin{block}{Heuristic 2}
+  \end{block}
 \end{block}
 
-\end{column} % End of the second column
-
+\end{column}
 %-----------------------------------------------------------------------------
-\begin{column}{\sepwid}\end{column} % Empty spacer column
+\begin{column}{\sepwid}\end{column}
 %-----------------------------------------------------------------------------
+
 \begin{column}{\onecolwid} % The third column
   \begin{block}{Experimental validation}
     \begin{figure}