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| author | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-02-05 20:54:19 -0500 |
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| committer | jeanpouget-abadie <jean.pougetabadie@gmail.com> | 2015-02-05 20:54:19 -0500 |
| commit | 1b76bfe6f4c0335fcf04a9f683949a3d412e05cd (patch) | |
| tree | be0f810efbb98111d03ce5d20b10808ae97015f3 | |
| parent | 4e12190e94c4a3e1e5fcd4a59db94bbe4222eae3 (diff) | |
| download | cascades-1b76bfe6f4c0335fcf04a9f683949a3d412e05cd.tar.gz | |
adding ref
| -rw-r--r-- | paper/sections/discussion.tex | 5 | ||||
| -rw-r--r-- | paper/sparse.bib | 14 |
2 files changed, 14 insertions, 5 deletions
diff --git a/paper/sections/discussion.tex b/paper/sections/discussion.tex index c4c70a3..25d9c53 100644 --- a/paper/sections/discussion.tex +++ b/paper/sections/discussion.tex @@ -11,11 +11,6 @@ Solving the Graph Inference problem with sparse recovery techniques opens new ve This model therefore falls into the 1-bit compressed sensing model \cite{Boufounos:2008} framework. Several recent papers study the theoretical guarantees obtained for 1-bit compressed sensing with specific measurements \cite{Gupta:2010}, \cite{Plan:2014}. Whilst they obtained bounds of the order ${\cal O}(n \log \frac{n}{s}$), no current theory exists for recovering positive bounded signals from bernoulli hyperplanes. This research direction may provide the first clues to solve the ``active learning'' problem: if we are allowed to adaptively \emph{choose} the source nodes at the beginning of each cascade, can we improve on current results? - - - - - \begin{comment} The Linear Threshold model can \emph{also} be cast a generalized linear cascade model. However, as we show below, its link function is non-differentiable and necessitates a different analysis. In the Linear Threshold Model, each node $j\in V$ has a threshold $t_j$ from the interval $[0,1]$ and for each node, the sum of incoming weights is less than $1$: $\forall j\in V$, $\sum_{i=1}^m \Theta_{i,j} \leq 1$. diff --git a/paper/sparse.bib b/paper/sparse.bib index e622b52..d1622fc 100644 --- a/paper/sparse.bib +++ b/paper/sparse.bib @@ -464,4 +464,18 @@ year = "2009" timestamp = {Tue, 12 Aug 2014 16:59:16 +0200}, biburl = {http://dblp.uni-trier.de/rec/bib/conf/webi/AdarA05}, bibsource = {dblp computer science bibliography, http://dblp.org} +} + +@inproceedings{Kleinberg:00, + author = {Jon M. Kleinberg}, + title = {The small-world phenomenon: an algorithm perspective}, + booktitle = {Proceedings of the Thirty-Second Annual {ACM} Symposium on Theory + of Computing, May 21-23, 2000, Portland, OR, {USA}}, + pages = {163--170}, + year = {2000}, + url = {http://doi.acm.org/10.1145/335305.335325}, + doi = {10.1145/335305.335325}, + timestamp = {Thu, 16 Feb 2012 12:06:08 +0100}, + biburl = {http://dblp.uni-trier.de/rec/bib/conf/stoc/Kleinberg00}, + bibsource = {dblp computer science bibliography, http://dblp.org} }
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