From bb4b6a3d52867600d003ca677ba37e0be1cce53c Mon Sep 17 00:00:00 2001
From: jeanpouget-abadie <jean.pougetabadie@gmail.com>
Date: Tue, 13 Jan 2015 11:58:37 -0500
Subject: maximum_likelihood_approach.tex

---
 notes/maximum_likelihood_approach.tex | 114 +++++++++++++++++++++++++++-------
 1 file changed, 91 insertions(+), 23 deletions(-)

(limited to 'notes/maximum_likelihood_approach.tex')

diff --git a/notes/maximum_likelihood_approach.tex b/notes/maximum_likelihood_approach.tex
index acc196b..18586e8 100644
--- a/notes/maximum_likelihood_approach.tex
+++ b/notes/maximum_likelihood_approach.tex
@@ -87,44 +87,112 @@ In conclusion, for $\delta>0$, $\lambda := 2 \sqrt{\frac{n^{\delta + 1} \log p}{
 
 \section*{Condition~\ref{eq:RSC_condition}}
 
-Finally, note that:
+Note that:
 \begin{equation}
 \nabla_{kj} f(\theta) = \sum_{i=1}^n \frac{b^i x_k^i x_j^i e^{\langle x^i, \theta \rangle}}{\left(1 - e^{\langle x^i, \theta \rangle} \right)^2} = \sum_{i=1}^n b^i x_k^i x_j^i \frac{\mathbb{P}(\text{node } \alpha \text { not infected})}{\mathbb{P}(\text{node } \alpha \text { infected})^2}
 \end{equation}
 
-\subsection*{First term}
-We are going to explicitate a constant $\gamma$ such that: $\forall \Delta \in {\cal C}, \Delta^T \cdot \nabla^2 f(\theta^*) \cdot \Delta \geq \gamma n \|\Delta\|_2^2$. If we denote $p_i := \mathbb{P}(\text{node } \alpha \text { infected})$, we have:
-\begin{equation}
-\Delta^T \cdot \nabla^2 f(\theta^*) \cdot \Delta = \sum_k \Delta_k^2 \left[ \sum_i b^i x_k^i \frac{1 - p_i}{p_i^2} \right] + 2 \sum_{k< j} \Delta_k \Delta_j \left[ \sum_i b^i x^i_k x^i_j \frac{1 - p_i}{p_i^2}\right]
-\end{equation}
 
-We are first going to find a lower-bound for $\sum_i b^i x_k^i \frac{1 - p_i}{p_i^2}$ by computing a lower-bound on its expectation and concluding with Hoeffding's inequality. If we only consider the first measurement of every cascade and we further suppose that $p_i < 1 - \eta$ no matter the configuration of active nodes (slightly less strong than correlation decay).
+We are going to explicitate a constant $\gamma$ such that: $\forall \Delta \in {\cal C}, \Delta^T \cdot \nabla^2 f(\theta^*) \cdot \Delta \geq \gamma n \|\Delta\|_2^2$. 
+
+\paragraph{Notation} Let $p_i := \mathbb{P}(\text{node } \alpha \text { infected})$. Let $Z^i_k := b^i x^i_k \frac{1-p_i}{p_i^2}$and let $Z^i_{k,j} := b^i x^i_k x^i_j \frac{1-p_i}{p_i^2}$. We also define $Z_k := \sum_i Z^i_k$ and $Z_{k,j} := \sum_i Z^i_{k,j}$.
+
+\begin{align}
+\Delta^T \cdot \nabla^2 f(\theta^*) \cdot \Delta & = \sum_k \Delta_k^2 \left[ \sum_i b^i x_k^i \frac{1 - p_i}{p_i^2} \right] + 2 \sum_{k< j} \Delta_k \Delta_j \left[ \sum_i b^i x^i_k x^i_j \frac{1 - p_i}{p_i^2}\right] \nonumber \\ 
+& = \sum_k \Delta_k^2 Z_k + 2 \sum_{k< j} \Delta_k \Delta_j Z_{k,j} \nonumber
+\end{align}
+
+
+
+\begin{lemma}
+\label{lem:first_term}
+Suppose that $\forall k$, $\mathbb{E}_{S(k)} \left[ \frac{1}{p_i} \right] \geq 1 + \alpha$, then with probability greater than $1 - 2 p e^{-2 \alpha^2 (1-c)^2p_{\min}^2 p_{\text{init}}^2 N}$, $$\forall k, \ Z_k > c \alpha p_{\text{init}} N$$
+\end{lemma}
+
+\begin{proof}
+Let $S(k)$ denote the active set conditioned on the fact that node $k$ is active.
+\begin{align}
+\nonumber
+\mathbb{E}[Z^i_k] & = \mathbb{P}(x^i_k = 1) \mathbb{E}_{S(k)} \left[ \mathbb{E}[b^i | S(k)] \frac{1 - p_i}{p_i^2} \right] \\ \nonumber
+& = p_{\text{init}} \left( \mathbb{E}_{S(k)} \left[ \frac{1}{p_i} \right] - 1 \right) \\ \nonumber
+& \geq \alpha p_{\text{init}} \quad \text{by assumption}
+\end{align}
+
+Note that $|Z^i_k| < \frac{1}{p_{\text{min}}^2}$ {\it a.s.}. By Hoeffding's first inequality, for $0<c <1$,
 
 \begin{align}
-\mathbb{E}\left(\sum_i b^i x_k^i \frac{1 - p_i} {p_i}^2 \right) & = \sum_i \mathbb{E} \left(x_k^i \frac{1 - p_i} {p_i}^2  \right) \nonumber \\
-& = \sum_i \mathbb{P}(b^i =1 | x_k^i =1) \mathbb{P}(x^i_k =1) \mathbb{E}\left(\frac{1 - p_i}{p_i^2} \middle| b^i =1 = x_k^i \right) \nonumber \\
-& = ATTENTION IL Y A ERREUR A LA LIGNE SUIVANTE: \\
-& \geq \min \left(1 , 1 - (1 - p_{init})^s \right)\cdot p_{init} \cdot \frac{\eta}{(1 - \eta)^2} \nonumber \\
-& \geq s p_{init}^2 \frac{\eta}{(1 - \eta)^2} \quad \text{si }s < \frac{1}{p_{init}} \nonumber \\
-& \geq p_{init} \frac{\eta}{(1 - \eta)^2} \quad \text{si }s > \frac{1}{p_{init}} \nonumber
+\nonumber
+\mathbb{P}\left(Z_k < c \alpha p_{\text{init}} N \right) & < 2 e^{- \frac{2(1-c)^2}{Nb^2} \left( \mathbb{E}_{S(k)} \left[ \frac{1}{p_i} \right] - 1 \right)^2} \\ \nonumber
+& < 2 e^{-2 \alpha^2 (1-c)^2p_{\min}^2 p_{\text{init}}^2 N}
 \end{align}
 
-We can conclude using the following Hoeffding inequality for independent random variables bounded by $[0, b_i]$ by noticing that our variables are bounded by above by $\frac{1 - p_{\min}}{p_{\min}^2}$
+We conclude by union bound.
+\end{proof}
+
+\begin{lemma}
+Suppose that $\forall k,j$, $1 + \alpha \leq \mathbb{E}_{S(k, j)} \left[ \frac{1}{p_i} \right] \leq 1 + \beta$, then with probability greater than $1 - pe^{-2 \alpha^2 (1-c)^2p_{\min}^2 p_{\text{init}}^4 N}$, $$\forall k,j, \ Z_{k,j} < c \beta p_{\text{init}}^2 N$$
+\end{lemma}
+
+\begin{proof}
+We follow the same reasoning as Lemma~\ref{lem:first_term}:
+\begin{align}
+\nonumber
+\mathbb{E}[Z^i_{k,j}] & = p_{\text{init}}^2 \left( \mathbb{E}_{S(k,j)} \left[ \frac{1}{p_i} \right] - 1 \right) \\ \nonumber
+& \leq \beta p_{\text{init}}^2 \quad \text{by assumption}
+\end{align}
+
+By Hoeffding's second inequality, for $0 < c < 1$,
+\begin{align}
+\nonumber
+\mathbb{P}\left(Z_{k,j} > c \beta p_{\text{init}}^2 N \right) & \leq e^{- \frac{2(1-c)^2}{Nb^2} \left( \mathbb{E}_{S(k,j)} \left[ \frac{1}{p_i} \right] - 1 \right)^2} \\ \nonumber
+& \leq e^{-2 \alpha^2 (1-c)^2p_{\min}^2 p_{\text{init}}^4 N} 
+\end{align}
+We conclude by union bound.
+\end{proof}
+
+\begin{proposition}
+Suppose that $\forall k,j$, $1 + \alpha \leq \mathbb{E}_{S(k, j)} \left[ \frac{1}{p_i} \right] \leq 1 + \beta$, then with probability greater than $1 - XXX$, condition~\ref{eq:RSC_condition} is met with $\gamma_n = \gamma n$ where $\gamma := p_{\text{init}}(\alpha - \beta p_{\text{init}})$
+\end{proposition}
+
 
 \paragraph{Hoeffding's inequality}
-\begin{equation}
+
+For $t \in \mathbb{R}$ and independent variables $Z_i$ such that $|Z_i|<b$ {\it a.s.}, we have:
+\begin{align}
 \label{eq:hoeffding_inequality}
-\mathbb{P} \left(\sum Z_i \geq \mathbb{E}[\sum Z_i] - t \right) \leq \exp\left(- \frac{2 N t^2}{b^2} \right)
-\end{equation}
+\mathbb{P} \left( \middle| \sum_{i=1}^N Z_i - \mathbb{E}\left[\sum_{i=1}^N Z_i \right] \middle| \geq t \right) \leq 2 \exp\left(- \frac{2 t^2}{N b^2} \right) \nonumber \\
+\mathbb{P} \left(\sum_{i=1}^N Z_i \geq \mathbb{E}\left[\sum_{i=1}^N Z_i \right] + t \right) \leq \exp\left(- \frac{2 t^2}{N b^2} \right) \nonumber
+\end{align}
+
+
+% \subsection*{First term}
+
+% We are first going to find a lower-bound for $\sum_i b^i x_k^i \frac{1 - p_i}{p_i^2}$ by computing a lower-bound on its expectation and concluding with Hoeffding's inequality. If we only consider the first measurement of every cascade and we further suppose that $p_i < 1 - \eta$ no matter the configuration of active nodes (slightly less strong than correlation decay).
+
+% \begin{align}
+% \mathbb{E}\left(\sum_i b^i x_k^i \frac{1 - p_i} {p_i}^2 \right) & = \sum_i \mathbb{E} \left(x_k^i \frac{1 - p_i} {p_i}^2  \right) \nonumber \\
+% & = \sum_i \mathbb{P}(b^i =1 | x_k^i =1) \mathbb{P}(x^i_k =1) \mathbb{E}\left(\frac{1 - p_i}{p_i^2} \middle| b^i =1 = x_k^i \right) \nonumber \\
+% & \geq \sum_{i=1}^n p_{\min} \cdot \min \left(1 , 1 - (1 - p_{init})^s \right)\cdot p_{init} \cdot \frac{\eta}{(1 - \eta)^2} \nonumber \\
+% & \geq s p_{init}^2 p_{\min} \frac{\eta}{(1 - \eta)^2} \quad \text{si }s < \frac{1}{p_{init}} \nonumber \\
+% & \geq p_{init} p_{\min} \frac{\eta}{(1 - \eta)^2} \quad \text{si }s > \frac{1}{p_{init}} \nonumber
+% \end{align}
+
+% We can conclude using the following Hoeffding inequality for independent random variables bounded by $[0, b_i]$ by noticing that our variables are bounded by above by $\frac{1 - p_{\min}}{p_{\min}^2}$
+
+% \paragraph{Hoeffding's inequality}
+% \begin{equation}
+% \label{eq:hoeffding_inequality}
+% \mathbb{P} \left(\sum Z_i \geq \mathbb{E}[\sum Z_i] - t \right) \leq \exp\left(- \frac{2 N t^2}{b^2} \right)
+% \end{equation}
 
-It follows that for $c<1$ with probability $1 - \exp \left( - n^3 c^2 s^2 p_{init}^4 p_{\min}^4 \frac{\eta^2}{(1 - \eta)^4} \right)$, we have that $$\sum_k \Delta_k^2 \left[ \sum_i b^i x_k^i \frac{1 - p_i}{p_i^2} \right] \geq  \gamma N =: (1 -c) s p_{init}^2 \frac{\eta}{(1 - \eta)^2} N$$
+% It follows that for $c<1$ with probability $1 - \exp \left( - n^3 c^2 s^2 p_{init}^4 p_{\min}^6 \frac{\eta^2}{(1 - \eta)^4} \right)$, we have that $$\sum_k \Delta_k^2 \left[ \sum_i b^i x_k^i \frac{1 - p_i}{p_i^2} \right] \geq  \gamma N =: (1 -c) s p_{init}^2 p_{\min} \frac{\eta}{(1 - \eta)^2} N$$
 
-\begin{remark}
-Would it be possible to extend this result using Azuma's inequality on Martingales to not just the first measurement of every cascade?
-\end{remark}
+% \begin{remark}
+% Would it be possible to extend this result using Azuma's inequality on Martingales to not just the first measurement of every cascade?
+% \end{remark}
 
-\subsection*{Second term}
-We are now going to find an upper-bound on the term $\sum_i b^i x^i_k x^i_j \frac{1 - p_i}{p_i^2}$.
+% \subsection*{Second term}
+% We are now going to find an upper-bound on the term $\sum_i b^i x^i_k x^i_j \frac{1 - p_i}{p_i^2}$.
 
 \section*{Conclusion}
 
-- 
cgit v1.2.3-70-g09d2

