\begin{algorithm}
\caption{Training GANs-based auction features generator}
    \begin{algorithmic}[1]
        \State $D_{train} \gets$ Initialize training set
        \While {$C(fake)>threshold$} \Comment{The critic can be optimized until $C(fake)$ is near $0$. } 
            \State Randomly select a discrete variable $c$ with equal probability
            \State Compute the probability mass function (PMF) of $c$
            \State Randomly select a state $i^*$ inherent to $c$ according its PMF
            \State Create the conditional vector $cond$ so that $\sum_i cond(i) = 1$ and $cond(i^*)=1$
            \For {$batch \in \{1,\dots,N_{batches}\}$} \Comment{Gradient descent with mini-batch}
                \State $real \gets d(c_{i^*}=1)\sim D_{train}$ \Comment{Sample batch of real examples respecting the constraint}
                \State $z \sim \mathcal{N}(0,1)$ \Comment{Sample noise}
                \State $fake \gets \tilde{d} \sim G(z)$ \Comment{Sample fake examples}
                \State $real \gets [real]\times 10$ \Comment{Stack input 10 times for Pac configuration}
                \State $fake \gets [fake] \times 10$
                \State $L^j \gets \big( C(fake_j) - C(real_j)\big) + CE(\tilde{c}, cond)$
                \State $L^{batch} \gets L^{batch} + \lambda(||\nabla L^{batch} ||_{2} - 1)^2$ \Comment{Apply gradient penalty}
                \State $w_{crit} \gets w_{crit} + Adam(\nabla_{w_{crit}}\frac{1}{m}\sum_i^m L^{batch}(i))$ \Comment{Updating $C$ with Adam}
                \If {$batch \bmod k = 0$} \Comment{Synchronicity, depends on $k$}
                    \State $w_{gen} \gets w_{gen} + Adam(\nabla_{w_{gen}}\frac{1}{m}\sum_i^m -C(G(z)))$ \Comment{Updating $G$ with Adam}
    				\EndIf
    			\EndFor
    		\EndWhile
    \end{algorithmic}
\label{alg:ctwgan}
\end{algorithm} 

\begin{algorithm}
\caption{Training GANs-based auction features generator}
\begin{algorithmic}
[1]
\State $D_{train} \gets$ Initialize training set
\While {$C(fake)>threshold$} \Comment{The critic can be optimized until $C(fake)$ is near $0$. }
\State Randomly select a discrete variable $c$ with equal probability
\State Compute the probability mass function (PMF) of $c$
\State Randomly select a state $i^*$ inherent to $c$ according its PMF
\State Create the conditional vector $cond$ so that $\sum_i cond(i) = 1$ and $cond(i^*)=1$
\For {$batch \in \{1,\dots,N_{batches}\}$} \Comment{Gradient descent with mini-batch}
\State $real \gets d(c_{i^*}=1)\sim D_{train}$ \Comment{Sample batch of real examples respecting the constraint}
\State $z \sim \mathcal{N}(0,1)$ \Comment{Sample noise}
\State $fake \gets \tilde{d} \sim G(z)$ \Comment{Sample fake examples}
\State $real \gets [real]\times 10$ \Comment{Stack input 10 times for Pac configuration}
\State $fake \gets [fake] \times 10$
\State $L^j \gets \big( C(fake_j) - C(real_j)\big) + CE(\tilde{c}, cond)$
\State $L^{batch} \gets L^{batch} + \lambda(||\nabla L^{batch} ||_{2} - 1)^2$ \Comment{Apply gradient penalty}
\State $w_{crit} \gets w_{crit} + Adam(\nabla_{w_{crit}}\frac{1}{m}\sum_i^m L^{batch}(i))$ \Comment{Updating $C$ with Adam}
\If {$batch \bmod k = 0$} \Comment{Synchronicity, depends on $k$}
\State $w_{gen} \gets w_{gen} + Adam(\nabla_{w_{gen}}\frac{1}{m}\sum_i^m -C(G(z)))$ \Comment{Updating $G$ with Adam}
\EndIf
\EndFor
\EndWhile
\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"
}
{
    "ToxicityFilter": {
        "text_to_toxicity_attributes": {
            "manuscript.bbl": {
                "toxicity_score": 0.011937759,
                "severe_toxicity_score": 0.0010728836,
                "identity_attack_score": 0.003311308,
                "insult_score": 0.0073798755,
                "profanity_score": 0.014395831,
                "threat_score": 0.007598988,
                "sexually_explicit_score": 0.011736456,
                "flirtation_score": 0.27859175
            },
            "manuscript.tex": {
                "toxicity_score": 0.01218908,
                "severe_toxicity_score": 0.0011634827,
                "identity_attack_score": 0.0037367835,
                "insult_score": 0.007664877,
                "profanity_score": 0.015454767,
                "threat_score": 0.007586042,
                "sexually_explicit_score": 0.01250316,
                "flirtation_score": 0.21971871
            },
            "utphys.bst": {
                "toxicity_score": 0.0154562555,
                "severe_toxicity_score": 0.0013160706,
                "identity_attack_score": 0.004384246,
                "insult_score": 0.008310881,
                "profanity_score": 0.017436003,
                "threat_score": 0.007624879,
                "sexually_explicit_score": 0.015452018,
                "flirtation_score": 0.34099135
            }
        },
        "reason": null
    }
}
{
    "num_done": {
        "table": 0,
        "figure": 0,
        "algorithm": 3,
        "plot": 0
    }
}
{
    "NonTrivialRenderingFilter": {
        "white_pixels_ratio": 90.32547615623267,
        "hash": "1f81809c94c0a83f",
        "most_frequent_color_ratio": 90.32547615623267
    }
}