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| author | Jean Pouget-Abadie <jean.pougetabadie@gmail.com> | 2015-04-14 18:43:39 -0400 |
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| committer | Jean Pouget-Abadie <jean.pougetabadie@gmail.com> | 2015-04-14 18:43:39 -0400 |
| commit | fc4339b5aa658ffdf0cb7c13b41e5d01f48002f1 (patch) | |
| tree | 90ea2fb3241508ecd4c4ddb0eaba363d36f9c6a5 /brainstorm | |
| parent | 22e254972aaf4cbb08d4925695d344c2835d6aae (diff) | |
| download | learn-optimize-fc4339b5aa658ffdf0cb7c13b41e5d01f48002f1.tar.gz | |
slightly cleaning up section on coverage function"
Diffstat (limited to 'brainstorm')
| -rw-r--r-- | brainstorm/results.tex | 17 |
1 files changed, 13 insertions, 4 deletions
diff --git a/brainstorm/results.tex b/brainstorm/results.tex index 5ca05f2..0b37049 100644 --- a/brainstorm/results.tex +++ b/brainstorm/results.tex @@ -632,7 +632,7 @@ $${\cal C} \equiv \{c : {\cal A} \mapsto \mathbb{R}^+ : \forall S \subset {\cal A}, c(S) = w(\cup_{S_i \in S} S_i) \text{ and } w(U) = k \}$$ Note that any coverage function from this family of coverage functions is -constant almost everywhere, equal to $w(x_1)+w(x_2) = k$: +constant almost everywhere, equal to $w(x_1)+w(x_2) = k$: \begin{itemize} \item $\forall S \neq A_1, @@ -640,11 +640,11 @@ A_2, c(S) = k$ \item $c(\{A_1\}) = w(x_1)$, and $c(\{A_2\}) = w(x_2)$ \end{itemize} -{\bf Easy to learn under any distribution} +\subsection{Easy to learn under any distribution} TODO -{\bf Hard to optimize under cardinality constraint with $K=1$} +\subsection{Hard to optimize under cardinality constraint with $K=1$} Given any distribution ${\cal D}$ on $2^U$ such that $\mathbb{P}(\{A_1\}) + \mathbb{P}(\{A_2\}) = o(n^{-c})$ for any constant $c$, @@ -666,17 +666,26 @@ $$\mathbb{P}_{S_1, \dots S_m \sim {\cal D}}\left(\mathbb{E}(f(\hat S)) \geq \frac{1}{n}OPT \right) \geq 1 - \frac{n^C}{2f(n)} \rightarrow 1 \text{ when } n \rightarrow +\infty$$ -This example is not fully satisfying for two reasons: +Note that the restriction on ${\cal D}$ is verified by many distributions. In +the case of the uniform distribution $ = \frac{1}{2^{n-1}}$ and in the case of +observing all sets of size $k>1$ uniformly at random: $=0$ This example is not +fully satisfying for two reasons: + \begin{itemize} \item the coverage function is not monotone. \item the sets we observe most do not satisfy the constraint. \end{itemize} + Note that even if we observe sets which \emph{almost} satisfy the cardinality constraint, such as all sets of size 2 and only such sets, we would still have no hope to output a set with expected value within $\frac{1}{n}$ of OPT\@ under cardinality constraint $K=1$. This example can be modified to address the second issue. +\subsection{Fixing second issue} + +We extend the previous example by slightly modifying ${\cal A}$ + \bibliography{optimize} \bibliographystyle{apalike} |
