% \begin{table}
% \centering
% \caption{Hessian analysis on RN18 trained with CIFAR10 10-shot. FP-BMA shows the lowest score on both maximal eigenvalue $\lambda_1$ and eigenvalue ratio $\lambda_1/\lambda_5$, proving it leads the model to flatter minima.}
% \vspace{-0.5em}
% \renewcommand{\arraystretch}{0.8}
% \adjustbox{width=1.0\linewidth}{
% \begin{tabular}{ccc}
% \toprule
% Method    & $\lambda_1 \downarrow$     & $\lambda_1 / \lambda_5 \downarrow$ \\ \toprule
% SGD      &   $559.62$  & $2.59$     \\
% SAM      &   $381.74$  & $2.23$     \\
% FSAM     &   $561.15$  & $2.24$     \\
% bSAM     &   $532.74$  & $2.09$      \\\midrule
% MOPED    &   $686.90$  & $2.41$      \\
% PTL      &   $559.16$  & $2.23$      \\
% E-MCMC   &   $540.83$  & $1.98$      \\
% SWAG     &   $602.34$  & $2.13$     \\
% F-SWAG   &   $362.33$  & $2.44$      \\
% FP-BMA   &   $\textbf{275.21}$  & $\textbf{1.69}$          \\ \bottomrule
% \end{tabular}
% }
% \label{tab:hessian}
% \end{table}

% \begin{table}
% \centering
% \caption{Hessian analysis on RN18 trained with CIFAR10 10-shot. FP-BMA shows the lowest score on both maximal eigenvalue $\lambda_1$ and eigenvalue ratio $\lambda_1/\lambda_5$, proving it leads the model to flatter minima.}
% \vspace{-0.5em}
% \renewcommand{\arraystretch}{0.8}
% \adjustbox{width=1.0\linewidth}{
% \begin{tabular}{ccccccccccc}
% \toprule
%  & SGD & SAM & FSAM & bSAM & MOPED & PTL & E-MCMC & SWAG & F-SWAG & FP-BMA \\ \toprule
% $\lambda_1 \downarrow$ & 559.62 & 381.74 & 561.15 & 532.74 & 686.90 & 559.16 & 540.83 & 602.34 & 362.33 & \textbf{275.21} \\
% $\lambda_1 / \lambda_5 \downarrow$ & 2.59 & 2.23 & 2.24 & 2.09 & 2.41 & 2.23 & 1.98 & 2.13 & 2.44 & \textbf{1.69} \\
% \bottomrule
% \end{tabular}
% }
% \label{tab:hessian}
% \end{table}


% \begin{table}[t]
% \centering
% \caption{Hessian analysis on RN18 trained with CIFAR10 10-shot. FP-BMA shows the lowest score on both maximal eigenvalue $\lambda_1$ and eigenvalue ratio $\lambda_1/\lambda_5$, proving it leads the model to flatter minima.}
% \vspace{-0.5em}
% \renewcommand{\arraystretch}{0.8}
% \adjustbox{width=1.0\linewidth}{
% \begin{tabular}{cccccc}
% \toprule
%  & SGD & SAM & FSAM & bSAM & MOPED \\ \toprule
% $\lambda_1 \downarrow$ & 559.62 & 381.74 & 561.15 & 532.74 & 686.90 \\
% $\lambda_1 / \lambda_5 \downarrow$ & 2.59 & 2.23 & 2.24 & 2.09 & 2.41 \\ \midrule\midrule
% & PTL & E-MCMC & SWAG & F-SWAG & \textbf{FP-BMA} \\ \midrule
% $\lambda_1 \downarrow$ & 559.16 & 540.83 & 602.34 & 362.33 & \textbf{275.21} \\
% $\lambda_1 / \lambda_5 \downarrow$ & 2.23 & 1.98 & 2.13 & 2.44 & \textbf{1.69} \\
% \bottomrule
% \end{tabular}
% }
% \label{tab:hessian}
% \end{table}


\begin{table*}[t]
\centering
\caption{Hessian analysis on ResNet18 trained with CIFAR10 10-shot. FP-BMA shows the lowest score on both $\lambda_1$ and $\lambda_1/\lambda_5$, proving it leads the model to flatter minima.}
\adjustbox{width=1.0\linewidth}{
\begin{tabular}{ccccccccccc}
\toprule
& SGD & SAM & FSAM & bSAM & MOPED & PTL & E-MCMC & SWAG & F-SWAG & \textbf{FP-BMA} \\ \midrule
$\lambda_1 \downarrow$ & 559.62 & 381.74 & 561.15 & 532.74 & 686.90 & 559.16 & 540.83 & 602.34 & 362.33 & \textbf{275.21} \\
$\lambda_1 / \lambda_5 \downarrow$ & 2.59 & 2.23 & 2.24 & 2.09 & 2.41 & 2.23 & 1.98 & 2.13 & 2.44 & \textbf{1.69} \\
\bottomrule
\end{tabular}
}
\label{tab:hessian}
\end{table*}