Small fixes

ecml-poster/figures/wetchicken.tex

@@ -45,7 +45,7 @@
             };
         \node[text width=25em] at (3.5, 0.6) {
                 \begin{description}[Challenges]
-                    \item[Dynamics] Agent in a flowing river
+                    \item[Dynamics] Wet-Chicken agent in a flowing river
                     \item[Goal] Get close to the waterfall
                     \item[State] $(x, y)$-position in $\R^2$
                     \item[Action] $(x, y)$-movement in $\R^2$

ecml-poster/poster.tex

@@ -152,6 +152,7 @@
                 \end{figure}
                 \begin{itemize}
                     \item DAGP with $K=2$ processes, one white noise process and one GP with an RBF kernel
+                        \smallskip
                     \item DAGP yields an explicit separation into outliers and signal for the training data
                 \end{itemize}
             \end{block}
@@ -168,11 +169,12 @@
                 \begin{itemize}
                     \item Given a data association problem, DAGP simultaneously learns
                         \begin{itemize}
+                            \normalsize
                             \item independent models $(\mat{F}^{\pix{k}}, \mat{Y}^{\pix{k}})$ for the $K$ processes
                             \item concrete assignments $\mat{A}$ of the training data to these processes
                             \item a factorization of the input space with respect to the relative importance $\mat{\alpha}$ of these processes
                         \end{itemize}
-                        % \smallskip
+                        \smallskip
                     \item DAGP's marginal likelihood combines $K$ regression problems and a classification problem
                         \begin{align}
                             \Prob*{\mat{Y} \given \mat{X}} &=
@@ -191,6 +193,7 @@
                         \end{align}
                 \end{itemize}
             \end{block}
+            \vspace*{-1ex}
             \begin{block}{Scalable inference}
                 \begin{itemize}
                     \item Learning based on doubly stochastic variational inference by \textcite{salimbeni_doubly_2017}
@@ -202,7 +205,9 @@
                             \MoveEqLeft = \prod_{k=1}^K\prod_{n=1}^N \Prob*{\mat{\alpha_n^{\pix{k}}} \given \mat{u_\alpha^{\pix{k}}}, \mat{x_n}}\Variat*{\mat{u_\alpha^{\pix{k}}}}
                             \prod_{k=1}^K \prod_{n=1}^N \Prob*{\mat{f_n^{\pix{k}}} \given \mat{u^{\pix{k}}}, \mat{x_n}}\Variat*{\mat{u^{\pix{k}}}}.
                         \end{align}
-                    \item We use a continuous relaxation of the assignment problem using concrete random variables in $\Variat{\mat{A}}$
+                        \smallskip
+                    \item We use a continuous relaxation of the assignment problem with concrete random variables in $\Variat{\mat{A}}$
+                        \smallskip
                     \item The joint variational bound is given by
                         \begin{align}
                             \Ell_{\text{DAGP}}
@@ -212,6 +217,7 @@
                             &\quad - \sum_{k=1}^K \KL*{\Variat*{\mat{u^{\pix{k}}}}}{\Prob*{\mat{u^{\pix{k}}} \given \mat{Z^{\pix{k}}}}}
                             - \sum_{k=1}^K \KL*{\Variat*{\mat{u_\alpha^{\pix{k}}}}}{\Prob*{\mat{u_\alpha^{\pix{k}}} \given \mat{Z_\alpha^{\pix{k}}}}}
                         \end{align}
+                        \smallskip
                     \item This bound can be sampled efficiently and factorizes along the data allowing for mini-batches
                         \smallskip
                     \item The predictive posterior can be efficiently approximated via sampling
@@ -227,7 +233,7 @@
         \separatorcolumn
         %
         \begin{column}{\colwidth}
-            \begin{block}{Wet-Chicken RL benchmark}
+            \begin{block}{Reinforcement Learning}
                 \centering
                 \includestandalonewithpath{figures/wetchicken}
             \end{block}