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up lect 17
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@ -3,5 +3,172 @@
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\chapter{Lecture 17 - 11-05-2020}
|
||||
|
||||
\section{Strongly convex loss functions}
|
||||
We will saw with OGD but we will see Support Vector Machine(SVM). Very popular learning model.
|
||||
\\
|
||||
We will see SVM next to see the part of linear predictor and also speak about Kernel function used with linear predictor to obtain non -linear classifier from a linear classifier.
|
||||
\\\\
|
||||
$\ell$ is $\sigma$-SC if $ \quad \forall u,w$:
|
||||
$$\ell(w) -\ell(u) \leq \nabla \ell(w)^T \, (w-u)- \frac{\sigma}{2} |\ w -u \|^2$$
|
||||
\\
|
||||
\subsection{OGD for Strongly Convex losses}
|
||||
|
||||
Init: $w_1 =(0,...,0)$\\
|
||||
For $t = 1,2...$\\
|
||||
$\qquad w_{t+1} = w_t -\frac{1}{\sigma \, t} \nabla \ell_t(w_t)$ \qquad $\eta_t = \frac{1}{\eta \, t}$
|
||||
\\
|
||||
(no projection steps)\\
|
||||
$$
|
||||
\ell_t (w_t) - \ell_t(u) \leq \nabla \ell_t (w_t)^T \, (w-u) - \frac{\sigma}{2} \| w_t - u \|^2 \ =
|
||||
$$
|
||||
$$
|
||||
= \ - \frac{1}{\eta_t} (w_{t+1} - w_t)^T \, (w_t-u) - \frac{\sigma}{2} \| w_t -u \|^2 \ =
|
||||
$$
|
||||
$$
|
||||
= \frac{1}{\eta_t} \left( \frac{1}{2} \| w_t - u\|^2 - \frac{1}{2} \| w_{t+1} - u \|^2 + \frac{1}{2} \| w_{t+1} - w_t \|^2 \right) - \frac{\sigma}{2} \| w_t -u \|^2
|
||||
$$
|
||||
$$
|
||||
R_T(u) \ \leq \ \frac{1}{2 \, \eta_1} \|w_1 -u \|^2
|
||||
\red{- \frac{1}{2 \, \eta_{T+1} } \| w_{T+1} - u \|^2}
|
||||
- \frac{\sigma
|
||||
}{2} \| w_1 -u \|^2 +
|
||||
$$
|
||||
$$
|
||||
+ \blue{ $\frac{1}{2} \sum_{t=1}^{T-1} \| w_{t+1} - u \|^2 \left( \frac{1}{\eta_{t+1}} - \frac{1}{\eta_t} - \sigma \right) $} +
|
||||
\red{\frac{1}{2} \| w_{T+1} - u \|^2 \left( \frac{1}{\eta_{T+1}}
|
||||
- \frac{1}{\eta_T} \right) }
|
||||
+ \frac{G^2}{2} \sum_{t=1}^T \eta_t
|
||||
$$
|
||||
where red terms cancel out, \blue{blue} (sum) instead is $0$ since $ \sigma(t+1) - \sigma \, t - \sigma$
|
||||
$$
|
||||
G = \max_t \| \nabla \ell_t(w_t) \|
|
||||
$$
|
||||
$$
|
||||
R_T(U) \leq \frac{1}{2} \left( \sigma - \sigma \right) \| w_1 -u \|^2 + \frac{G^2}{2} \sum_{t=1}^T \frac{1}{\sigma \, t} \ = \
|
||||
$$
|
||||
$$
|
||||
R_T(U) \leq \frac{G^2}{2} \sum_{t=1}^T \frac{1}{\sigma \, t} \
|
||||
$$
|
||||
We know that $\sum_{t=1}^T \frac{1}{T} \leq \ln \left( T+1 \right)$
|
||||
so:
|
||||
$$
|
||||
R_T(U) \ \leq \ \frac{G^2}{2 \, \sigma} \, \ln \left( T+1 \right)
|
||||
$$
|
||||
$$
|
||||
\frac{R_T(U)}{T} \ \ \textbf{vanishes at rate } \ \frac{\ln \, T}{T} < < \frac{1}{\sqrt[]{T}} \ \ \textbf{provided } max_t \| \nabla \ell_t (w_t) \| \ \textbf{remains bounded}
|
||||
$$
|
||||
We assume it in special case.
|
||||
\\\\
|
||||
Where are these SC losses?
|
||||
\\
|
||||
Minimising strongly convex version of standard convex losses helps a lot.
|
||||
\\
|
||||
We will see how Regularitation imply Stability. Before studing SVM and stability we going to do something before.
|
||||
\\
|
||||
\subsection{Relate sequential risk and statistical risk}
|
||||
It is important: I have this algorith that control sequential risk and regret but I am also courious to use this algorithms.
|
||||
\\\\
|
||||
We assume:\\
|
||||
Data $(x_t,y_) $ drawm i.i.d. from fixed unknown $D$.
|
||||
\\
|
||||
Convex loss function $\ell$.
|
||||
\\ For example compare square loss and hinge loss(convex upper bound on $0$-$1$ $loss$:
|
||||
$$
|
||||
\ell(\hat{y}, y = (\hat{y},y)^2 \qquad \ell(\hat{y},y) = \left[ 1- \hat{y} \, y \right]_+
|
||||
$$
|
||||
We will focus on linear predictors $h(x) = f(w^T \, x) $\quad (easily to analise with OGD framework).
|
||||
\\
|
||||
Risk $\ell_D(w) =$ \expt{\ell(w^T \, X, Y) }
|
||||
\\
|
||||
where $ \hat{y} = w^T \, X$
|
||||
\\
|
||||
Assume we have a training set $S$ of example $(X_1,Y_1)...(X_m,Y_m)$ \qquad (in maiusc since are random sequence of data point from a distribution)
|
||||
\\
|
||||
$$
|
||||
Convex \quad \ell_t(w) = \ell(w^T \, X_t, Y_t) \qquad t= 1,...m
|
||||
$$
|
||||
Became a sequence of convex losses.
|
||||
\\
|
||||
I run OGD on $\ell_1,\ell_2,..., \ell_m$ and get $w_1,..., w_m \quad \|w_t\| \leq U$\\
|
||||
OGD projects onto: $$\{ U \in \barra{R}^d : \| u \| \leq U \} \qquad U^* = arg \min_{u: \|u\| \leq U} \ell_D(u)$$
|
||||
where $U^*$ is the best linear predictor in class.\\
|
||||
So i take a bunch of predictors but i need one, so I take the average of those (since the expected value is convex):
|
||||
$$
|
||||
\bar{w} = \frac{1}{m}\sum_{t=1}^m w_t
|
||||
$$
|
||||
I want to study the variance error:
|
||||
$$
|
||||
\ell_D(\bar{w}) - \ell_D(u^*) \ ?
|
||||
$$
|
||||
I am using Online Learning.
|
||||
\\
|
||||
Using Jensen inequality:
|
||||
$$
|
||||
\ell_D(\bar{w}) = \barra{E} \left[ \ell(\bar{w}^T \, X, Y \right] \ \leq \ \barra{E} \left[ \frac{1}{m} \sum_{t=1}^m \ell(w_t^T \, X, Y) \right] = \frac{1}{m} \sum_t \barra{E} \left[ \ell(w_t^T \, X, Y) \right]
|
||||
$$
|
||||
where $\barra{E} \left[ \ell(w_t^T \, X, Y) \right]$ is equals to $\ell_D(w_t)$
|
||||
\\
|
||||
$$
|
||||
\ell_D(\bar{w}) \leq \frac{1}{m} \sum_{t=1}^n \ell_D(w_t) \qquad \textbf{for any given training set } (x_1,y_1)...(x_m,y_m) \quad
|
||||
$$
|
||||
I want to look at the difference:
|
||||
$$
|
||||
\ell_D(w_t) - \ell(w_t^T \, X_t, Y_t)
|
||||
$$
|
||||
$
|
||||
\ell_D = \ $\expt{\ell(w^T_t \, X, Y) }$
|
||||
$\\
|
||||
Now I fix $t-1$ example in the training set \quad $(X_1, Y_1) ...(X_{t-1},Y_{t-1})$
|
||||
\\
|
||||
$w_t$ is \bred{determined} by $(X_1, Y_1),...(X_{t-1},Y_{t-1})$\\
|
||||
$(X_t,Y_t)$ is distribuited like any $(X,Y) \sim D$
|
||||
$$
|
||||
\barra{E}_{t-1} \left[ \, \cdot \, \right] = \barra{E} \left[ \, \cdot \, | (X_1,Y_1)...(X_{t-1}, Y_{t-1} \right] \qquad z_t = \ell_D(w_t) - \ell( w_t^T \, X_t, Y_t)
|
||||
$$
|
||||
$$
|
||||
\frac{1}{m} \sum_{t=1}^m \barra{E}_{t-1} \left[ Z_t \right] = 0
|
||||
$$
|
||||
I want to show the average of $\ell_D(w_t)$ is equal to average of $\ell(w^T_t \, X_t, Y)$
|
||||
\\
|
||||
I want to prove:
|
||||
$$
|
||||
\frac{1}{m} \sum_{t=1}^m \ell_D (w_t) \ \leq \ \red{\frac{1}{m} \sum_{t=1}^m \ell(w_t^T \, X_t, Y_t)}+ \ \sqrt[]{\frac{1}{m} \, \ln \frac{1}{\delta}} \qquad \textit{ with high probability w.r.t. S}
|
||||
$$
|
||||
where (red part) is the sequential risk of OGD.
|
||||
$$
|
||||
\frac{1}{m} \sum_{t=1}^m Z_t \leq \sqrt[]{\frac{1}{m} \, \ln \frac{1}{\delta}} \qquad \textbf{with prob. at least $1-\delta$}
|
||||
$$
|
||||
I know that $\barra{E}_{t-1} \left[ Z_t\right] = 0 $
|
||||
$$ |Z_t| \in [0, M] \quad \Rightarrow \quad \frac{1}{m} \sum_{t=1}^m Z_t \leq M \sqrt[]{\frac{2}{m} \, \ln \frac{1}{\delta}} \quad \textit{w.p $1-\delta$}
|
||||
$$\\
|
||||
Version of Chernoff-Hoffdiwg bounds for sums of dependent random variables.
|
||||
\\
|
||||
$$
|
||||
\frac{1}{m} \sum_{t=1}^m \ell_D(w_t) \ \leq \ \frac{1}{m} \sum_{t=1}^m \ell_t(w_t) + M \ \sqrt[]{\frac{2}{m} \ln \frac{1}{\delta}} \quad \textit{ w.p $1-\delta$}
|
||||
$$
|
||||
\\
|
||||
This tells me that$ \ell_D(\bar{w})$ is controlled by the sequential risk of OGD + $O \left(\frac{1}{\sqrt[]{m}} \right)$
|
||||
\\
|
||||
Variance Error for $(w^T \, x-y)^2 \qquad \| x_t\| \leq X, \quad |y_t| \leq U \, X$
|
||||
$$
|
||||
G = \max_t \| \nabla \ell_t (w_t) \| \leq 4\, (U \, X)^2
|
||||
$$
|
||||
$$
|
||||
\ell_D(\bar{w}) \leq \red{ \min_{u: \|u\| \leq U} \frac{1}{m} \sum_{t=1}^m \ell_D(u) + 8 \, (U \, X)^2 \sqrt[]{\frac{2}{m}}}+ 4 \, (U \, X)^2 \ \sqrt[]{\frac{2}{m} \, \ln\frac{1}{\delta}}
|
||||
$$
|
||||
where \red{red} is \bred{OGD analysis}
|
||||
$$
|
||||
\ell_D(\bar{w}) \ \leq \ \min \frac{1}{m} \sum_{t=1}^m \ell_t(u) + 12 \, (U \, X)^2 \sqrt[]{\frac{2}{m} \, \ln \frac{1}{\delta}} \quad \textit{with prob. $1-\delta$}
|
||||
$$
|
||||
By C-H bounds:
|
||||
$$
|
||||
where \quad \min \frac{1}{m} \sum_{t=1}^m \ell_t(u) \ \leq \ \frac{1}{m} \sum_{t=1}^m \ell_t(u^*) \ \leq \ \ell_D(u^*) + 4\, (U \, X)^2 \ \sqrt[]{\frac{1}{2 \, m} \ln \, \frac{1}{\delta}}
|
||||
$$
|
||||
where the sum is the test error of $u^*$
|
||||
\\
|
||||
At the end:
|
||||
$$
|
||||
\ell_D(\bar{w}) \leq \ell_D(u^*) + 16 \, (U \, X)^2 \ \sqrt[]{\frac{1}{m} \, \ln \, \frac{1}{\delta}} \qquad \textit{w.p $1-\delta$}
|
||||
$$
|
||||
Even with m large, I can run it since i bounded in the small "ball".
|
||||
\end{document}
|
||||
|
|
@ -183,15 +183,18 @@
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This is pdfTeX, Version 3.14159265-2.6-1.40.21 (MiKTeX 2.9.7300 64-bit) (preloaded format=pdflatex 2020.4.13) 17 MAY 2020 17:34
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[107] (lectures/lecture17.tex
|
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Chapter 17.
|
||||
) [108
|
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|
||||
] (lectures/lecture18.tex
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Chapter 18.
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Output written on main.pdf (118 pages, 2599444 bytes).
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\contentsline {section}{\numberline {16.1}Analysis of Perceptron in the non-separable case using OGD framework.}{102}%
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\contentsline {subsection}{\numberline {16.1.1}Strongly convex loss functions}{106}%
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\contentsline {chapter}{\numberline {17}Lecture 17 - 11-05-2020}{108}%
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\contentsline {chapter}{\numberline {18}Lecture 18 - 12-05-2020}{109}%
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\contentsline {section}{\numberline {18.1}Kernel functions}{109}%
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\contentsline {subsection}{\numberline {18.1.2}Kernels implements feature expansion (Efficiently}{110}%
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\contentsline {section}{\numberline {18.2}Gaussian Kernel}{111}%
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\contentsline {section}{\numberline {17.1}Strongly convex loss functions}{108}%
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\contentsline {subsection}{\numberline {17.1.1}OGD for Strongly Convex losses}{108}%
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\contentsline {subsection}{\numberline {17.1.2}Relate sequential risk and statistical risk}{109}%
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\contentsline {chapter}{\numberline {18}Lecture 18 - 12-05-2020}{112}%
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\contentsline {section}{\numberline {18.1}Kernel functions}{112}%
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\contentsline {subsection}{\numberline {18.1.1}Feature expansion}{112}%
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\contentsline {subsection}{\numberline {18.1.2}Kernels implements feature expansion (Efficiently}{113}%
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\contentsline {section}{\numberline {18.2}Gaussian Kernel}{114}%
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Loading…
Reference in New Issue
Block a user