Bandwidth freeze

content/appendixs/appendix_bandwidth_freeze.tex

@@ -0,0 +1,219 @@
+\Cref{sec:roll-kde-backward} derives \eqref{eq:kde-grad-combined} by treating the
+bandwidths $v_0, v_1$ as constants during differentiation. This appendix makes explicit
+what is given up by that choice: we derive the gradient that results from
+differentiating through the bandwidths as well (\Cref{sec:bandwidth-freeze-general}),
+then give two counterexamples (\Cref{sec:bandwidth-freeze-cex1,sec:bandwidth-freeze-cex2})
+showing that the resulting correction is not a vanishing refinement of
+\eqref{eq:kde-grad-combined} but can cancel it exactly or reverse its sign entirely.
+
+\section{The Fully Adaptive Gradient}
+\label{sec:bandwidth-freeze-general}
+
+In order to derive the gradient w.r.t.\ the bandwidth parameter, we now write
+$v_k = v_k(\mathbf{s}^{(k)})$ for the bandwidth parameter of class $k$, now a
+differentiable function of that class's scores $\mathbf{s}^{(k)} =
+\bigl(f_\theta(\mathbf{x}_j)\bigr)_{\mathbf{x}_j \in \mathcal{B}_k}$, as
+\Cref{sec:kde-bandwidth} in fact computes it (via ISJ or the Silverman fallback) on every
+forward pass. Differentiating the sigmoid kernel~\eqref{eq:sigmoid-kernel} with respect
+to $v_k$ and applying the identity~\eqref{eq:sigmoid-identity} gives the
+bandwidth-sensitivity term
+
+\begin{equation}
+  \rho_k \;:=\; \frac{\partial \hat{F}_k(\tau)}{\partial v_k}
+  \;=\; \frac{1}{v_k\,|\mathcal{B}_k|}\sum_{\mathbf{x}_j \in \mathcal{B}_k}
+    \bigl(\tau - f_\theta(\mathbf{x}_j)\bigr)\,\sigma_k'\bigl(\tau - f_\theta(\mathbf{x}_j)\bigr).
+  \label{eq:bandwidth-sensitivity}
+\end{equation}
+
+\paragraph{Case $y_i = 1$.}
+$\tau$ does not depend on $\mathcal{B}_1$, so the chain rule adds a single term to the
+direct contribution already derived in~\eqref{eq:kde-grad-y1}:
+\begin{equation}
+  \frac{dL}{df_\theta(\mathbf{x}_i)}\bigg|_{y_i=1}
+  = \underbrace{-\frac{1}{|\mathcal{B}_1|}\,\sigma_1'\bigl(\tau - f_\theta(\mathbf{x}_i)\bigr)}
+      _{\text{\eqref{eq:kde-grad-y1}}}
+  \;+\; \rho_1 \cdot \frac{dv_1}{df_\theta(\mathbf{x}_i)}.
+  \label{eq:bandwidth-freeze-y1}
+\end{equation}
+
+\paragraph{Case $y_i = 0$.}
+Here $\tau$ is defined implicitly by $\hat{F}_0(\tau) = 1-\alpha$, and $v_0$ is itself a
+function of $\mathbf{x}_i$'s class. Totally differentiating the defining equation and
+comparing to~\eqref{eq:kde-grad-tau} shows the fixed-bandwidth result picks up the same
+kind of correction:
+\begin{equation}
+  \frac{d\tau}{df_\theta(\mathbf{x}_i)}
+  = \underbrace{\frac{\sigma_0'\bigl(\tau-f_\theta(\mathbf{x}_i)\bigr)}
+      {\sum_{\mathbf{x}_j \in \mathcal{B}_0}\sigma_0'\bigl(\tau-f_\theta(\mathbf{x}_j)\bigr)}}
+      _{\text{\eqref{eq:kde-grad-tau}}}
+  \;-\; \frac{|\mathcal{B}_0|\,\rho_0}
+      {\sum_{\mathbf{x}_j \in \mathcal{B}_0}\sigma_0'\bigl(\tau-f_\theta(\mathbf{x}_j)\bigr)}
+    \cdot \frac{dv_0}{df_\theta(\mathbf{x}_i)},
+  \label{eq:bandwidth-freeze-tau}
+\end{equation}
+and chaining through $\partial \hat{F}_1(\tau)/\partial\tau$ from~\eqref{eq:kde-dF1-dtau}:
+\begin{equation}
+  \frac{dL}{df_\theta(\mathbf{x}_i)}\bigg|_{y_i=0}
+  = \underbrace{\eqref{eq:kde-grad-combined}\big|_{y_i=0}}_{\text{unchanged}}
+  \;-\; \left(\frac{1}{|\mathcal{B}_1|}\sum_{\mathbf{x}_j\in\mathcal{B}_1}
+      \sigma_1'\bigl(\tau-f_\theta(\mathbf{x}_j)\bigr)\right)
+  \frac{|\mathcal{B}_0|\,\rho_0}
+      {\sum_{\mathbf{x}_j \in \mathcal{B}_0}\sigma_0'\bigl(\tau-f_\theta(\mathbf{x}_j)\bigr)}
+    \cdot \frac{dv_0}{df_\theta(\mathbf{x}_i)}.
+  \label{eq:bandwidth-freeze-y0}
+\end{equation}
+
+In both cases, the fully adaptive gradient equals~\eqref{eq:kde-grad-combined} plus
+a correction proportional to $dv_k/df_\theta(\mathbf{x}_i)$. For the Silverman fallback of
+\Cref{sec:kde-bandwidth}, with class mean $\bar{f}_k$ and class variance
+$\hat\sigma_k^2$,
+\begin{equation}
+  \frac{dv_k}{df_\theta(\mathbf{x}_i)}
+  = -\frac{v_k\,\bigl(f_\theta(\mathbf{x}_i) - \bar{f}_k\bigr)}{|\mathcal{B}_k|\,\hat\sigma_k^2},
+  \qquad \mathbf{x}_i \in \mathcal{B}_k,
+  \label{eq:silverman-dv-df}
+\end{equation}
+which the two counterexamples below instantiate directly.
+
+Notably, ISJ has no closed form, and so would require re-derivation through its numerical computation.
+The Silverman instance already suffices to establish our claim.
+
+\section{Counterexample 1: Exact Cancellation}
+\label{sec:bandwidth-freeze-cex1}
+
+Take $\mathcal{B}_0 = \{-1, 1\}$ with any bandwidth $v_0$, and
+$\mathcal{B}_1 = \{0, b\}$ for a free scalar $b > 0$, with $\alpha = 1/2$.
+
+By symmetry of $\mathcal{B}_0$ about $0$ and evenness of the sigmoid
+kernel~\eqref{eq:sigmoid-kernel}, $\hat{F}_0(0) = \tfrac{1}{2}\bigl[\sigma(1;v_0) +
+\sigma(-1;v_0)\bigr] = \tfrac{1}{2}$ for \emph{every} $v_0$, so $\tau = 0$ exactly,
+independent of the bandwidth.
+
+Silverman's rule on $\mathcal{B}_1 = \{0,b\}$ ($|\mathcal{B}_1|=2$,
+$\hat\sigma_1 = b/2$) gives $h_1 = \kappa b$ with $\kappa := 1.06 \cdot 2^{-1/5}/2$, i.e.\
+$v_1 = 1/(\kappa b)$ — the bandwidth scales linearly with $b$.
+
+\paragraph{True adaptive loss.} Substituting $v_1 = 1/(\kappa b)$,
+\[
+  L(b) = \hat{F}_1(0) = \tfrac{1}{2}\bigl[\sigma(0;v_1) + \sigma(-b;v_1)\bigr]
+       = \tfrac{1}{2}\Bigl[\tfrac{1}{2} + \sigma(-1/\kappa)\Bigr]
+       = \tfrac{1}{4} + \tfrac{1}{2}\sigma(-1/\kappa),
+\]
+using $v_1 b = 1/\kappa$, a constant. The loss does not depend on $b$ at all:
+\[
+  \frac{dL}{db} = 0 \quad\text{exactly, for every } b > 0.
+\]
+Rescaling $b$ rescales its own bandwidth identically, so the standardized KDE shape at
+$\tau = 0$ never moves.
+
+A loss that derives to a parameter gradient of zero is a serious failure mode for
+gradient-based learning: it gives optimization nothing to act on, regardless of where
+$b$ is initialized. In this example, once $b > 1$, this batch's negative-class scores
+$\{-1,1\}$ are confined to $[-1,1]$, so scores above $1$ are a regime reserved solely
+for class $1$, a more separated region of score space.
+Under the fully adaptive gradient, $b$ would never move toward it: $dL/db = 0$
+identically, so gradient descent stalls wherever $b$ starts, however far from that
+regime.
+
+\paragraph{What \eqref{eq:kde-grad-combined} gives instead.} Plugging directly into the
+frozen-bandwidth term of~\eqref{eq:bandwidth-freeze-y1} (equivalently
+\eqref{eq:kde-grad-y1}),
+\[
+  \frac{dL}{db}\bigg|_{\text{\eqref{eq:kde-grad-combined}}}
+  = -\tfrac{1}{2}\,\sigma_1'(-b; v_1),
+\]
+which is strictly negative for every finite $b, v_1 > 0$, since $\sigma_1' > 0$
+everywhere. \Cref{eq:kde-grad-combined} therefore reports a nonzero, consistently signed
+gradient.
+We see that using the frozen-bandwidth term, eventually, $b$ would be pushed
+higher, approaching $b>1$, and achieving better separability.
+
+\section{Counterexample 2: Persistent Sign Reversal}
+\label{sec:bandwidth-freeze-cex2}
+
+Take the same $\mathcal{B}_0 = \{-1,1\}$ (so again $\tau = 0$ for any $v_0$), and
+$\mathcal{B}_1 = \{1+\varepsilon,\ b\}$ for a small fixed $\varepsilon > 0$ and a free
+scalar $b > 1+\varepsilon$. We define $\delta := b - (1+\varepsilon) > 0$.
+Silverman's rule again gives $h_1 = \kappa\delta$, $v_1 = 1/(\kappa\delta)$, with $\kappa$ as above.
+
+\paragraph{What \eqref{eq:kde-grad-combined} gives.} Plugging into
+\eqref{eq:kde-grad-y1} and simplifying with the sigmoid-identity~\eqref{eq:sigmoid-identity},
+\begin{equation}
+  \frac{dL}{db}\bigg|_{\text{\eqref{eq:kde-grad-combined}}}
+  = -\tfrac{1}{2}\,\sigma_1'(-b;v_1)
+  = -\frac{1}{8\kappa\delta}\operatorname{sech}^2\!\left(\frac{b}{2\kappa\delta}\right)
+  \;<\; 0 \qquad \text{for every } \delta > 0.
+  \label{eq:cex2-frozen}
+\end{equation}
+This is the intuitively correct sign. $\mathbf{x}_i$ already scores above $\tau$, so
+increasing $b$ further should only increase separability, therefore decreasing the loss.
+
+\paragraph{The fully adaptive gradient.} Substituting the same configuration into
+\eqref{eq:bandwidth-freeze-y1} requires $\rho_1$ and $dv_1/db$. Since
+$v_1 = 1/(\kappa\delta)$ depends on $b$ only through $\delta = b-(1+\varepsilon)$,
+\begin{equation}
+  \frac{dv_1}{db} = -\frac{1}{\kappa\delta^2} = -\frac{v_1}{\delta}.
+  \label{eq:cex2-dv1-db}
+\end{equation}
+Expanding \eqref{eq:bandwidth-sensitivity} for $\mathcal{B}_1 = \{1+\varepsilon, b\}$
+and again applying the sigmoid-identity~\eqref{eq:sigmoid-identity},
+\begin{equation}
+  \rho_1 = -\frac{1}{8}\left[(1+\varepsilon)\operatorname{sech}^2\!\left(\frac{(1+\varepsilon)v_1}{2}\right)
+    + b\operatorname{sech}^2\!\left(\frac{bv_1}{2}\right)\right].
+  \label{eq:cex2-rho1}
+\end{equation}
+Substituting \eqref{eq:cex2-dv1-db} and \eqref{eq:cex2-rho1} into \eqref{eq:bandwidth-freeze-y1}
+alongside \eqref{eq:cex2-frozen}, the $\operatorname{sech}^2(b/(2\kappa\delta))$ terms
+combine and $b - \delta = 1+\varepsilon$ cancels the remainder, leaving:
+\begin{equation}
+  \frac{dL}{db}\bigg|_{\text{full}}
+  = \frac{1+\varepsilon}{8\kappa\delta^2}\left[
+      \operatorname{sech}^2\!\left(\frac{b}{2\kappa\delta}\right)
+      + \operatorname{sech}^2\!\left(\frac{1+\varepsilon}{2\kappa\delta}\right)
+    \right].
+  \label{eq:cex2-full}
+\end{equation}
+Since $\operatorname{sech}^2(x) > 0$ for every finite $x$ and $1+\varepsilon,\, \kappa,\, \delta^2 > 0$, \eqref{eq:cex2-full} is
+strictly positive for every $\delta > 0$, the opposite sign to
+\eqref{eq:cex2-frozen}, established here for the entire domain $b > 1+\varepsilon$.
+
+\Cref{tab:cex2-values} confirms \eqref{eq:cex2-full} numerically (cross-checked against
+an independent finite-difference derivative of $L(b)$) across an exponential range of
+$\delta$, from just off the degenerate boundary $\delta \to 0^+$ to well-separated, for
+$\varepsilon = 0.05$ ($\kappa \approx 0.4614$):
+
+\begin{center}
+\begin{tabular}{rrrr}
+\toprule
+$\delta$ & $b$ & \eqref{eq:kde-grad-combined} (frozen) & fully adaptive, \eqref{eq:cex2-full} \\
+\midrule
+$10^{-2}$ & 1.06   & $-1.82\times 10^{-98}$ & $+1.86\times 10^{-95}$ \\
+$10^{-1}$ & 1.15   & $-1.62\times 10^{-10}$ & $+1.66\times 10^{-8}$ \\
+$10^{0}$  & 2.05   & $-1.24\times 10^{-2}$  & $+1.09\times 10^{-1}$ \\
+$10^{1}$  & 11.05  & $-8.30\times 10^{-3}$  & $+3.68\times 10^{-3}$ \\
+$10^{2}$  & 101.05 & $-9.81\times 10^{-4}$  & $+3.87\times 10^{-5}$ \\
+\bottomrule
+\end{tabular}
+\label{tab:cex2-values}
+\end{center}
+
+The fully adaptive gradient is positive at every finite
+$\delta > 0$, never matching \eqref{eq:cex2-frozen}'s sign. Gradient
+descent on the fully adaptive loss would push an already well-separated positive sample
+back toward the decision threshold, throughout its entire domain, weakening separability
+against what we are trying to achieve.
+
+\section{Discussion}
+\label{sec:bandwidth-freeze-discussion}
+
+The two counterexamples isolate the same correction term,
+$\rho_k \cdot dv_k/df_\theta(\mathbf{x}_i)$, in two regimes: in
+\Cref{sec:bandwidth-freeze-cex1} it exactly cancels the gradient we need to continue learning,
+and in \Cref{sec:bandwidth-freeze-cex2} it inverts it entirely. In neither case is it
+simply a small correction to \eqref{eq:kde-grad-combined}. It is not bounded by, or shrinking
+relative to, the term it is added to. With this term added, we could no longer guarantee
+gradient sign correctness (\Cref{obs:gradient-sign-correctness}): \Cref{sec:bandwidth-freeze-cex2}
+gives a sample with $y_i=1$ whose gradient is positive throughout its entire domain, in
+direct violation of the guarantee. \Cref{sec:roll-kde-backward} therefore adopts
+\eqref{eq:kde-grad-combined}, with $v_0, v_1$ frozen at their forward-pass values, as the
+definition of the ROLL gradient.

content/method/method.tex

@@ -22,13 +22,21 @@ The \emph{true positive rate} (TPR) and \emph{false positive rate} (FPR) at thre
   \operatorname{TPR}(\tau) &= P\!\bigl(f_\theta(\mathbf{x}) > \tau \mid y = 1\bigr), \\
   \operatorname{FPR}(\tau) &= P\!\bigl(f_\theta(\mathbf{x}) > \tau \mid y = 0\bigr).
 \end{align}
-For $\alpha \in (0,1)$, let $\tau^*(\alpha)$ denote the threshold achieving
-$\operatorname{FPR}(\tau^*(\alpha)) = \alpha$, and write
-$\operatorname{TPR}(\alpha) = \operatorname{TPR}(\tau^*(\alpha))$ for the true positive rate
-at that operating point. The curve $\alpha \mapsto \operatorname{TPR}(\alpha)$ is the ROC curve.
+
+Conversely, we may denote the inverse operations $\operatorname{FPR}^{-1}$ and $\operatorname{TPR}^{-1}$ to be:
+
+\begin{align}
+  \operatorname{TPR^{-1}}\!\bigl(P\!\bigl(f_\theta(\mathbf{x}) > \tau \mid y = 1\bigr)\bigr) &= \tau, \\
+  \operatorname{FPR^{-1}}\!\bigl(P\!\bigl(f_\theta(\mathbf{x}) > \tau \mid y = 0\bigr)\bigr) &= \tau .
+\end{align}
+
+
+For $\alpha \in (0,1)$, let $\tau = \operatorname{FPR}^{-1}(\alpha)$ denote the threshold achieving
+$\operatorname{FPR}(\tau) = \alpha$.
+The curve $\alpha \mapsto \operatorname{TPR}(\operatorname{FPR^{-1}}(\alpha))$ is the ROC curve.
 The primary learning objective is to maximize its area:
 \begin{equation}
-  \max_\theta\;\operatorname{AUC}(f_\theta) = \int_0^1 \operatorname{TPR}(\alpha)\,d\alpha.
+  \max_\theta\;\operatorname{AUC}(f_\theta) = \int_0^1 \operatorname{TPR}(\operatorname{FPR}^{-1}(\alpha))\,d\alpha.
   \label{eq:objective-auc}
 \end{equation}
 Maximizing AUC is the natural objective under class imbalance and label noise, where no
@@ -410,6 +418,14 @@ Positive-class samples are weighted by their individual kernel
 PDF at $\tau$; negative-class samples are weighted by the mean positive-class kernel
 PDF scaled by each sample's share of the negative-class kernel mass.
 
+Equation~\eqref{eq:kde-grad-combined} is derived by treating the bandwidths $v_0, v_1$
+as fixed constants, even though \Cref{sec:kde-bandwidth} re-estimates them from the
+batch scores on every forward pass. We adopt this as a deliberate stop-gradient
+convention: as \Cref{obs:gradient-sign-correctness} shows, it is exactly what
+guarantees every per-sample gradient points in the direction that improves separation.
+Differentiating fully through the bandwidth estimator can break that guarantee, as
+Appendix~\ref{appendix:bandwidth-freeze} shows by counterexample.
+
 %------------------------------------------------
 
 \subsection{Properties of the ROLL Formulation}
@@ -470,6 +486,34 @@ average of 99 per-threshold objectives each satisfying \eqref{eq:grad-balance},
 is preserved by linearity of summation.
 \end{corollary}
 
+\paragraph{Gradient sign correctness.}
+\label{para:gradient-sign-correctness}
+
+A third structural consequence of \eqref{eq:kde-grad-combined} is that every per-sample
+gradient points in the direction that improves separation, never against it.
+
+\begin{observation}[Gradient sign correctness]
+\label{obs:gradient-sign-correctness}
+For every $\mathbf{x}_i \in \mathcal{B}$,
+\[
+  y_i = 1 \;\Longrightarrow\; \frac{\partial \mathcal{L}_{\text{ROLL-TPR@FPR}}}{\partial f_\theta(\mathbf{x}_i)} \leq 0,
+  \qquad
+  y_i = 0 \;\Longrightarrow\; \frac{\partial \mathcal{L}_{\text{ROLL-TPR@FPR}}}{\partial f_\theta(\mathbf{x}_i)} \geq 0.
+\]
+\end{observation}
+
+This follows directly from \eqref{eq:kde-grad-combined}: for $y_i = 1$ the gradient is
+$-\sigma_1'(\tau-f_\theta(\mathbf{x}_i))/|\mathcal{B}_1|$, and $\sigma_1' \geq 0$
+everywhere; for $y_i = 0$ the gradient is a product of two nonnegative quantities (a
+mean of $\sigma_1'$ values and a normalized $\sigma_0'$ ratio). Gradient descent
+therefore never pushes a positive-class score down, nor a negative-class score up, a
+minimal requirement for any loss intended to drive separation.
+
+This guarantee is specific to the frozen-bandwidth gradient~\eqref{eq:kde-grad-combined}.
+Differentiating fully through the adaptive bandwidth of \Cref{sec:kde-bandwidth} can
+break it: Appendix~\ref{appendix:bandwidth-freeze} gives an exact counterexample where
+the fully adaptive gradient violates this sign guarantee.
+
 \paragraph{Gradient locality.}
 \label{para:gradient-locality}
 

flake.nix

@@ -55,33 +55,23 @@
       fontsConf = pkgs.makeFontsConf {
         fontDirectories = [ pkgs.culmus ];
       };
-    in
-    {
-      devShells.${system}.default = pkgs.mkShell {
-        buildInputs = [
-          (pkgs.texlive.combine texPkgs)
-          pkgs.culmus
-          pkgs.fontconfig
-          pkgs.poppler-utils
-          (pkgs.python3.withPackages (ps: [ ps.sympy ps.numpy ps.matplotlib ps.scipy ]))
-        ];
-        shellHook = ''
-          export FONTCONFIG_FILE="${fontsConf}"
-        '';
-      };
 
-      packages.${system}.default = pkgs.stdenvNoCC.mkDerivation rec {
-        name = "roll-thesis";
-        src = self;
-        buildInputs = [
+      thesisBuildInputs = [
         pkgs.coreutils
         pkgs.fontconfig
         (pkgs.texlive.combine texPkgs)
         pkgs.culmus
       ];
+
+      mkThesisDoc = { name, texFile }:
+        let pdfFile = builtins.replaceStrings [ ".tex" ] [ ".pdf" ] texFile;
+        in pkgs.stdenvNoCC.mkDerivation {
+          inherit name;
+          src = self;
+          buildInputs = thesisBuildInputs;
           phases = [ "unpackPhase" "buildPhase" "installPhase" ];
           buildPhase = ''
-          export PATH="${pkgs.lib.makeBinPath buildInputs}";
+            export PATH="${pkgs.lib.makeBinPath thesisBuildInputs}";
             mkdir -p .cache/texmf-var .cache/fontconfig
 
             # Build a fontconfig pointing at culmus with a writable cache
@@ -100,13 +90,32 @@ EOF
 
             env TEXMFHOME=.cache TEXMFVAR=.cache/texmf-var \
             latexmk -xelatex -f --interaction=nonstopmode \
-          main.tex || true
-          test -f main.pdf
+            ${texFile} || true
+            test -f ${pdfFile}
           '';
           installPhase = ''
             mkdir -p $out
-          cp main.pdf $out/
+            cp ${pdfFile} $out/
           '';
         };
+    in
+    {
+      devShells.${system}.default = pkgs.mkShell {
+        buildInputs = [
+          (pkgs.texlive.combine texPkgs)
+          pkgs.culmus
+          pkgs.fontconfig
+          pkgs.poppler-utils
+          (pkgs.python3.withPackages (ps: [ ps.sympy ps.numpy ps.matplotlib ps.scipy ]))
+        ];
+        shellHook = ''
+          export FONTCONFIG_FILE="${fontsConf}"
+        '';
+      };
+
+      packages.${system} = {
+        default = mkThesisDoc { name = "roll-thesis"; texFile = "main.tex"; };
+        mentor  = mkThesisDoc { name = "roll-thesis-mentor"; texFile = "mentor.tex"; };
+      };
     };
 }

main.tex

@@ -167,6 +167,8 @@
 \Crefname{observation}{Observation}{Observations}
 \Crefname{assumption}{Assumption}{Assumptions}
 \Crefname{remark}{Remark}{Remarks}
+\crefname{appendix}{appendix}{appendices}
+\Crefname{appendix}{Appendix}{Appendices}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %For Hebrew table of contents: (\addcontentsline is used to add section and subsections to the Hebrew toc. For some reason, the page number is reversed only for chapter, thus we use \addtocontents for them)
 \makeatletter
@@ -256,6 +258,7 @@
 \addtocontents{tocheb}{\contentsline{chapter}{\protect\numberline{\chapnumforhebrewtoc}{דיון ומסקנות}}{{\beginL\thepage\endL}}}
 \input{content/discussion_and_conclusions/discussion_and_conclusions}
 
+\appendix
 \begin{appendices}
 
 \chapter{Gaussian ROLL: Forward Pass and Gradient Derivation}
@@ -303,6 +306,11 @@
 \addtocontents{tocheb}{\contentsline{chapter}{\protect\numberline{נספח ט}{\quad\qquadהוכחת טענת איזון הגרדיאנט}}{{\beginL\thepage\endL}}}
 \input{content/appendixs/appendix_grad_balance}
 
+\chapter{KDE ROLL: Adaptive-Bandwidth Gradients and the Freezing Convention}
+\label{appendix:bandwidth-freeze}
+\addtocontents{tocheb}{\contentsline{chapter}{\protect\numberline{נספח י}{\quad\qquadKDE ROLL: גרדיאנט רוחב פס אדפטיבי ומוסכמת ההקפאה}}{{\beginL\thepage\endL}}}
+\input{content/appendixs/appendix_bandwidth_freeze}
+
 \end{appendices}
 
 %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%

mentor.tex

@@ -0,0 +1,258 @@
+\documentclass[12pt,a4paper,oneside,onecolumn]{book}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% remove unnecessary warnings
+\usepackage{silence}
+\WarningFilter{caption}{Unknown document class}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Hebrew + English via XeLaTeX:
+\usepackage{fontspec}
+\usepackage{polyglossia}
+\setmainlanguage{english}
+\setotherlanguage{hebrew}
+% Culmus OpenType fonts for Hebrew (exact fc family names)
+\newfontfamily\hebrewfont[Script=Hebrew]{Frank Ruehl CLM}
+\newfontfamily\hebrewfontsf[Script=Hebrew]{Miriam CLM}
+\newfontfamily\hebrewfonttt[Script=Hebrew]{Miriam Mono CLM}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Set page numbering with no headers (according to guidelines):
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\lhead{}
+\chead{\thepage}
+\rhead{}
+\lfoot{}
+\cfoot{}
+\rfoot{}
+\renewcommand{\headrulewidth}{0pt}
+\fancypagestyle{plain}{\pagestyle{fancy}}
+\setlength{\headheight}{15pt}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Sub-figures/tables parenthesis:
+
+% For parenthesis in sub-figure reference
+\usepackage[labelformat=simple]{subcaption}
+\renewcommand\thesubfigure{(\alph{subfigure})}
+
+% For parenthesis before sub-table caption
+\renewcommand\thesubtable{(\alph{subtable})}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Misc packages:
+
+\usepackage{textcomp} %loaded before "gensymb" to avoid annoying warnings
+\usepackage{gensymb} %Degree symbol
+\usepackage{mathtools} %cases*
+\usepackage{amssymb} %Math letters
+\usepackage[shortlabels]{enumitem}
+\usepackage[multiple]{footmisc}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%theorem, corollary and lemma definitions:
+
+\usepackage[amsmath, thmmarks]{ntheorem}
+
+\newtheorem{theorem}{Theorem}
+\newtheorem{corollary}[theorem]{Corollary}
+\newtheorem{lemma}[theorem]{Lemma}
+\newtheorem{proposition}[theorem]{Proposition}
+\newtheorem{observation}[theorem]{Observation}
+\newtheorem{assumption}[theorem]{Assumption}
+\newtheorem{remark}[theorem]{Remark}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%proof definition:
+
+\theoremstyle{nonumberplain}
+\theoremheaderfont{\itshape}
+\theorembodyfont{\normalfont}
+\theoremseparator{.}
+\theoremsymbol{\ensuremath{\square}}
+\newtheorem{proof}{Proof}
+
+% Change back to default values - needed to repeat assumptions
+\theoremstyle{plain}
+\theoremheaderfont{\normalfont\bfseries}
+\theorembodyfont{\itshape}
+\theoremseparator{}
+\theoremsymbol{}
+
+\usepackage{chngcntr}
+\counterwithin{theorem}{chapter}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Misc:
+\DeclareMathOperator*{\argmax}{arg\,max}
+\usepackage[ruled,vlined,algochapter]{algorithm2e}
+\usepackage{csquotes}
+
+% Show numbers also for subsubsection
+\setcounter{secnumdepth}{4}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% bibliography:
+
+\usepackage[numbers,sort,compress,square]{natbib}
+\bibliographystyle{bibliography/unsrtnat_adjusted_for_bgu_thesis}
+
+%add bib to toc
+\usepackage[nottoc]{tocbibind}
+
+%add command for citation with possessive apostrophe
+\newcommand\citepossessive [1]{\citeauthor{#1}'s\ \cite{#1}}
+
+%Natbib cites cannot be broken to two lines
+\makeatletter
+\def\NAT@spacechar{~}% NEW
+\makeatother
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% paragraph settings:
+\setlength{\parindent}{0pt}
+\setlength{\parskip} {5pt}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% drawing and graphics:
+\usepackage{tikz}
+\usetikzlibrary{matrix,positioning}
+
+\usepackage{float} %Not sure it will be needed
+
+\usepackage{graphicx}
+\graphicspath{ {content/images/} }
+\usepackage{booktabs}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\usepackage{titling}
+\usepackage[titletoc]{appendix}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Code listings:
+\usepackage{listings}
+\lstset{
+  basicstyle=\ttfamily\footnotesize,
+  breaklines=true,
+  breakatwhitespace=false,
+  showstringspaces=false,
+  frame=single,
+  numbers=left,
+  numberstyle=\tiny\ttfamily,
+  numbersep=6pt,
+  xleftmargin=18pt,
+  framexleftmargin=18pt,
+  tabsize=4,
+  captionpos=b,
+  keepspaces=true,
+  columns=flexible,
+}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Not included since 'hyperref' and 'bookmark' are too problematic with Hebrew
+% \usepackage[hidelinks]{hyperref}
+% \usepackage{bookmark}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% cleveref:
+\usepackage[capitalise]{cleveref}
+%Workaround for babel and cleveref (for citing few equations)
+\makeatletter
+\def\@@number#1{#1}
+\makeatother
+
+\Crefname{observation}{Observation}{Observations}
+\Crefname{assumption}{Assumption}{Assumptions}
+\Crefname{remark}{Remark}{Remarks}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%set spacing between lines
+\usepackage{setspace}
+\doublespacing
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%Title and names for the article
+\newcommand{\thesistitle}{\textbf{Noise-Robust Gradient-Based Learning Utilizing Rate Optimized Likelihood Loss}}
+\newcommand{\thesistitlehe}{\textbf{למידה עמידה לרעש מבוססת גרדיאנט תוך שימוש בפונקציית הפסד קירוב-הסתברות}}
+
+\newcommand{\thesisauthorname}{\textbf{Aner Zakobar}}
+\newcommand{\thesisauthornamehe}{\textbf{ענר זקובר}}
+
+\newcommand{\thesissupervisername}{\textbf{Prof. Lior Rokach, Prof. Aryeh Kontorovich}}
+\newcommand{\thesissupervisernamehe}{\textbf{פרופ. ליאור רוקח, פרופ. אריה קונטורוביץ}}
+
+\newcommand{\thesismonth}{\textbf{September}}
+\newcommand{\thesismonthhe}{\textbf{ספטמבר}}
+\newcommand{\thesisyear}{\textbf{2026}}
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\begin{document}
+
+\begin{titlepage}
+  \centering
+  \vspace*{3cm}
+  {\LARGE\bfseries Noise-Robust Gradient-Based Learning\\[0.5em]
+  Utilizing Rate Optimized Likelihood Loss}\\[2.5em]
+  {\large Aner Zakobar}\\[0.5em]
+  {\normalsize Supervised by: Prof.\ Lior Rokach, Prof.\ Aryeh Kontorovich}\\[3em]
+  \rule{0.5\textwidth}{0.4pt}\\[1em]
+  {\large\itshape Excerpt: Chapter~3 \& Appendices A, C--F, I}\\[0.5em]
+  {\normalsize Note: cross-references to omitted chapters appear as \textbf{??}}\\[1em]
+  \rule{0.5\textwidth}{0.4pt}
+  \vfill
+  {\large September 2026}
+\end{titlepage}
+
+\tableofcontents
+\clearpage
+
+\mainmatter
+
+%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%
+
+\setcounter{chapter}{2}
+\chapter{Method}
+\label{chapter:method}
+\input{content/method/method}
+
+%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%
+
+\begin{appendices}
+
+\setcounter{chapter}{0}
+\chapter{Gaussian ROLL: Forward Pass and Gradient Derivation}
+\label{appendix:gauss-grad}
+\input{content/appendixs/appendix_gauss_grad}
+
+\setcounter{chapter}{2}
+\chapter{KDE ROLL: Gradient Derivation}
+\label{appendix:kde-grad}
+\input{content/appendixs/appendix_kde_grad}
+
+\setcounter{chapter}{3}
+\chapter{KDE ROLL: Sigmoid Kernel -- Explicit Equations}
+\label{appendix:kde-sigmoid}
+\input{content/appendixs/appendix_kde_sigmoid}
+
+\setcounter{chapter}{4}
+\chapter{KDE ROLL: Bisection for CDF Inversion}
+\label{appendix:kde-bisection}
+\input{content/appendixs/appendix_kde_bisection}
+
+\setcounter{chapter}{5}
+\chapter{KDE ROLL: Newton--Raphson Inversion (Superseded)}
+\label{appendix:kde-nr}
+\input{content/appendixs/appendix_kde_nr}
+
+\setcounter{chapter}{8}
+\chapter{Proof of the Gradient Balance Proposition}
+\label{appendix:grad-balance}
+\input{content/appendixs/appendix_grad_balance}
+
+\end{appendices}
+
+%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%
+\bibliography{bibliography/thesis}
+
+\end{document}