From ff6fb0a97f833e9502c3ca19590cb117e4628e80 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Fri, 3 Apr 2015 17:23:51 -0400
Subject: Add meeting notes

---
 results.tex | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)

(limited to 'results.tex')

diff --git a/results.tex b/results.tex
index 8735aa3..4968e4b 100644
--- a/results.tex
+++ b/results.tex
@@ -542,6 +542,32 @@ A}\backslash {\cal B}$.
 
 {\bf TODO:} A cleaner simpler example would be nice.
 
+\section{Meeting notes: 04.03.2015}
+
+Consider the following function:
+\begin{displaymath}
+    g(S) = \max\left\{\sqrt{n}|X\cap S|, |S|\right\}
+\end{displaymath}
+where $|X|=\sqrt{n}$. Assume that you are given polynomially many samples where
+each element is included with probability $1/2$. Then with high probability all
+the samples will have size roughly $n/2$, so you will observe $g(S)=|S|$ with
+high probability.
+
+\paragraph{Claim 1:} $g$ is PAC-learnable because if you output $|S|$ then you
+will be correct with high probability is $S$ is drawn from the same
+distribution as above.
+
+\paragraph{Claim 2:} $g$ is not optimizable under budget $\sqrt{n}$ because you
+never learn anything about $X$.
+
+\paragraph{Open Question:} Cook a similar example where $g$ is submodular and
+where you are observing sets of the same size as your budget.
+
+\paragraph{Positive results:} Try to obtain guarantees about learning
+parameters of parametric submodular functions and show whether or not these
+guarantees are sufficient for optimization. First look at learning weights in
+a cover function. Maybe facility location? Sums of concave over modular are
+probably too hard because of the connection to neural networks.
 
 
 \bibliography{optimize} \bibliographystyle{apalike}
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