\begin{algorithm}
\caption{K-folds cross-validation BidNet training procedure}
    \begin{algorithmic}[1]
        \State $D \gets \{D_1,\dots,D_K\}$ \Comment{Initialize K-folds}
        \State $loss^{*}\gets \infty$ \Comment{Initialize best model}
        \For {$fold \in D$}
            \State $reset(w_{BidNet})$ \Comment{Reset parameters before entering each new fold}
            \State $D_{val}\gets D(fold)$, $D_{train}\gets D(-fold)$
            \While {has not converged}
                \For {$batch \in \{1,\dots,N_{batches}\}$} \Comment{Gradient descent with mini-batch}
                    \State $d \sim D_{train}$ \Comment{Sample batch of real examples}
                    \State $\hat{\theta}\gets BidNet(d)$
                    \State $L^{train}\gets m^{-1}\sum_i NLL(\hat{\theta})_i$ \Comment{compute NLL on training batch}
                    \State $w \gets w + Adam(\nabla L^{train})$ \Comment{Update BidNet}
                \EndFor
                \State $converged \gets ES(L^{val})$ \Comment{Early stopping}
                \State $L^{val}\gets n^{-1} \sum_j NLL(BidNet(D_{val}))_j$ \Comment{compute NLL on validation fold}
                \If {$L^{val}<loss^{*}$}
                    \State $loss^{*} \gets L^{val}$
                    \State save model
                \EndIf
			\EndWhile
		\EndFor
    \end{algorithmic}
\label{alg:bidnet}
\end{algorithm} 

\begin{algorithm}
\caption{K-folds cross-validation BidNet training procedure}
\begin{algorithmic}
[1]
\State $D \gets \{D_1,\dots,D_K\}$ \Comment{Initialize K-folds}
\State $loss^{*}\gets \infty$ \Comment{Initialize best model}
\For {$fold \in D$}
\State $reset(w_{BidNet})$ \Comment{Reset parameters before entering each new fold}
\State $D_{val}\gets D(fold)$, $D_{train}\gets D(-fold)$
\While {has not converged}
\For {$batch \in \{1,\dots,N_{batches}\}$} \Comment{Gradient descent with mini-batch}
\State $d \sim D_{train}$ \Comment{Sample batch of real examples}
\State $\hat{\theta}\gets BidNet(d)$
\State $L^{train}\gets m^{-1}\sum_i NLL(\hat{\theta})_i$ \Comment{compute NLL on training batch}
\State $w \gets w + Adam(\nabla L^{train})$ \Comment{Update BidNet}
\EndFor
\State $converged \gets ES(L^{val})$ \Comment{Early stopping}
\State $L^{val}\gets n^{-1} \sum_j NLL(BidNet(D_{val}))_j$ \Comment{compute NLL on validation fold}
\If {$L^{val}<loss^{*}$}
\State $loss^{*} \gets L^{val}$
\State save model
\EndIf
\EndWhile
\EndFor
\end{algorithmic}
\end{algorithm}
{
    "title": "implementing a hierarchical deep learning approach for simulating   multi-level auction data",
    "id": "2207.12255",
    "abstract": "we present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. the complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. our methodology combines deep generative modeling (dgm) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. this approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of dgm as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative ai.",
    "categories": "econ.gn q-fin.ec",
    "doi": "",
    "created": "2022-07-25",
    "updated": "2024-02-15",
    "authors": [
        "igor sadoune",
        "andrea lodi",
        "marcelin joanis"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2207.12255"
}
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