Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Authors: Zeyu Yang
Han Yu
Peikun Guo
Khadija Zanna
Xiaoxue Yang
Akane Sano
Venue: Accepted by TMLR ()

Abstract: Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this research, we introduce a novel tabular diffusion model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes, such as sex and race.
The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms existing methods for synthesizing tabular data on fairness metrics such as demographic parity ratio and equalized odds ratio, achieving improvements of over $10\%$. Our implementation is available at https://github.com/comp-well-org/fair-tab-diffusion.

Published in Transactions on Machine Learning Research (02/2025)
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Zeyu Yang Deportment of Electrical and Computer Engineering Rice University
Saanyann@@@maad com
Han Yu
hy29 edu
Deportment Electrical and Computer Engineering Rice University
Peikun Guo
pg34 com
Department of Electrical and Computer Engineering Rice University
Khadija Zanna
kz35 edu
Department of Electrical and Computer Engineering
Rice University
Xiaoxue Yang
yolendaasyong@@maiilccom com
Department of Computing Imperial College London
Akane Sano
Cansue.sano@rcce..
Department of Electrical and Computer Engineering
Rice University
Reviewed on OpenReview: https: openreview. net/ forum? id= duRysCamYQ
Abstract
Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias in the training dataset and generate biased synthetic data, which may influence discriminatory actions. In this rescarch, we introduce a novel tabular diffusion model that incorporates sen- sitive guidance to generate fair synthetic data with balanced joint, distributions of the target label and sensitive attributes, such sex and race. The empirical results demonstrate that our method effectively mitigates bias in training data while maintaining the quality of the generated samples. Furthermore, we provide evidence that our approach outperforms exist- ing methods for synthesizing tabular data on fairness metrics such as demographic parity ratio and equalized odds ratio, achieving improvements of over 10%. Our implementation com/ compp//el--orggfffrrtttaaaa tari--rrtrttr--aaiiiffoe
available at
1 Introduction
Tabular data synthesis, particularly when involving mixed-type variables, presents unique challenges due to the need to simultaneously handle continuous and discrete features. Recent advances in diffusion models (Sohl-Dickstein et al.) 2015 Ho et al., 2020) are now being leveraged to tackle these challenges. Diffusion models have demonstrated remarkable performance in generative modeling for various tasks, espocially text- to-image synthesis. Researchers use diffusion frameworks to model images paired with text descriptions and show promising results (Nichol et al. 2021 Saharia al. 2022 Advancing further from diffusion models in

---

Published in Transactions on Machine Learning Research (02/2025)
pixel space, latent diffusion frameworks optimize image representation in a low-dimensional space, thereby enhancing synthetic image quality and efficiency (Rombach et al. 2022: Ramesh et al. 2022). The impressive results of diffusion models in image synthesis extended to generating sequential data such as audio and time scries (Kng et al.) 2020: Rasul et al., 2021 Li ct al. 2022). This advancement has gradually overshadowed generalive adversarial workorks, which were previously a significant focus in generative modeling, as disfu- sion models consistently demonstrate superior performance across diverse tasks (Dhariwal & Nichol 2021:
Stypuikowski et al. 2023;
Mazé & Ahmmed 2023).
Researchers have recently been motivated by the versatility of diffusion models to explore the potential in synthesizing tabular data (Kotelnikov ct al. 2023 He et al. 2023). Tabular data often involves sensitive user information, which raises significant privacy concerns when used for model training. Moreover, such datasets suffer from limitations in size and imbalanced class distribution (Jesus et al.) 2022), leading to challenges in training robust and fair machine learning models. To address these issues, there is a growing demand for effective tabular data synthesis. However, generative modcling of tabular data challenging due to the combination of discrete and continuous features and their varying value distributions. Interpolation- based techniques, such as SMOTE (Chawla et al.) 2002), are simple and effective but they pose privacy concerns as they generate new data points through direct interpolation of existing data. Rescarchers have used adversarial generative networks and variational autoencoders to generate tabular data with promising results (Ma et al. 2020 Xu et al. 2019). Research on diffusion models for tabular data is ongoing, and the primary challenge is to handle continuous and discrete features simultaneously. In Austin et al. (2021), the authors suggest using diffusion frameworks for discrete features with Markov transition matrices. Recent developments such TabDDPM (Kotelnikov et. al., 2023) and CoDi (Lee et al., 2023), which combine Markov transition functions (called diffusion kernels) for continuous and discrete features, mark significant progress in this field and underscore the potential of diffusion models as a comprehensive solution for tabular data synthesis.
Despite the success of diffusion models in various generative tasks, ethical concerns rogarding potential bias and unsale content. in the synthetic data persist. Generative models are inherently data-driven, thereby propagating these biases into the synthetic data. Studies such as fair diffusion (Friedrich et al.) 2023) and safe latent diffusion (Schramowski et al., 2023) have shown that latent diffusion models, particularly in text- to-image synthesis, can create images biased toward certain privileged groups and may contain inappropriate elements, such as nudity and violence. In response, they propose methods to apply hazardous or sensitive guidance in latent space and then eliminate bias and offensive elements in the synthetic images. In addition to text and images, tabular datasets frequently exhibit notable bias, often stemming from issues related to the disproportionate representation of sensitive groups. For instance, some dat astas exhibit siguificant imbalances in demographic groups regarding sensitive attributes such as sex and race (Le Quy et al. 2022). Training machine learning models on biased tabular data can result in discriminatory outcomes for underrepresented groups. However, in our literature review, research on the safety and fairness of diffusion models in areas beyond text-to-image synthesis is limited. Although diffusion models have shown remarkable performance
in tabular data synthesis (Sattarov et al. 2023 He et al., 2023: Kotelnikov et al. 2023; Lee et al. 2023 Kim
et al. 2022), they are prone to learning and replicating these biases in training data.
Existing tabular diffusion models are typically either unconditional or conditioned on a single target vari- able. For example, CoDi (Loe et al. 2023) leverages two interconnected diffusion models to separately process continuous and discrete variables while maintaining their inter-variable correlations through co-evolutionary conditioning and contrastive learning. Although CoDi efficiently captures inter-variable relationships, its conditioning mechanism remains restricted to single-label guidance. As a result, CoDi does not explicitly address fairness considerations in the presence of sensitive attributes. In contrast, we introduce a novel tabu- lar diffusion model that approximates mixed-type tabular data distributions, conditioned on both outcomes and multiple sensitive attributes. Our method extends tabular diffusion models from label-only conditioning to multivariate feature-level conditioning for enhancing fairness. To promote fairness, our approach generates data with balanced joint. distributions of the target label and sensitive attributes. Specifically, our approach integrates sensitive guidance into the tabular diffusion model and extends it to manage multiple sensitive at attributes, such as sex and race. We utilize a U-Net architecture to approximate the data distributions in the latent space during the reverse diffusion process conditioned on various variables, including the target
2

---

Published in Transactions on Machine Learning Research (02/2025)
label and sensitive attributes. To ensure fairness while preserving the integrity of the original data, apply the element-wise comparison technique from safe latent diffusion to regulate the sensitive guidance within an appropriate range. Additionally, we employ balanced sampling techniques to reduce disparities in group representation within the synthetic data. Given that our method leverages diffusion frameworks tailored for tabular data and supports multiple variables for conditional generation of fair tabular data, it also has the potential for extension to multi-modal data generation with fairness considerations using diffusion models. In summary, our main contributions are as follows: We employ balanced sampling techniques to reduce disparities in group representation within the synthetic data. In summary, our main contributions are follows:
We introduce, to our knowlodge, the first diffusion- basod framework tailored learn distributions of mixed-type tabular data with the extension from label-only conditioning to multivariate feature-level conditioning for fairness.
2. We generate labular data that is balanced for a predefined set of sensitive attribules, addressing inherent biases in the data.
3. Our model demonstrates strong performance in terms of fidelity and diversity of synthetic data, while surpassing existing models in fairness metrics such as demographic parity ratio and equalized oddes ratio by more than 10% on average across three experimental datasets.
The remainder of this paper is organized follows: Section provides overview of related work on fairness-aware machine learning and diffusion models. Section 3 discusses the relevant background of mixed- type modeling with diffusion models. In Section 4 we present our approach to multivariate conditioning and balanced sampling. In Section 5 we illustrate the experiments and present the results. Section we conclude our findings and discuss their implications.
t discusses
the limitations of our method. Finally, in Section
2
Related Work
2.1 Bias in Machine Learning
The concept of bias in machine learning that involves assigning significance to specific features to enhance overall generalization is essential for the effective performance of models (Mehrabi et al. 2021). However, it is crucial to acknowledge that bias in machine learning can also have nogative implications. Negative bias is an inacourate assumption made by machine learning algorithms that reflects systematic or historical prejudices against certain groups of people (Zanna et al., 2022). Decisions derived from such biased algorithms can lead to adverse effects, particularly impacting specific social groups defined by factors such as sex, age, disability, race, and more.
This concept of bias and fairness in machine learning has been widely studied, the aim being to mitigate algorithrnic bias of sensitive attributes from machine learning models (Mehrabi et al., 2021 Pessach & Shmueli 2022). Many methods for bias mitigation in classification tasks have been proposed, and they can be classified into three categories: pre-processing, in-processing, and post-processing methods. Pre- processing methods (Kamiran & Calders 2012 Calmon et al., 2017) transform the collected data to remove discrimination and train machine learning models on discriminationn-feee dalasets. In-processing mods (Zhang et al. 2018 Agarwal et al., 2018 Kamishima et al. 2011 Zafar et al. 2017 Kamishima et al. 2012) regulate machine lcarning algorithms by incorporating fairness constraints or regularization terms into the objective functions. Lastly, post-processing methods (Hardt et al. 2016 Pleiss el al. 2017), implermented after training machine learning models on collected datasets, directly override the predicted labels to improve fairness.
2.2 Fair Data Synthesis
aware generative models typically operate as pre-processing methods. One such method proposod by Xu et al. (2018) introduced faimess-awaree adversarial generative networks that employ a fair discrimi- nator to maintain equality in the joint probability of features and labels conditioned on subgroups within
3

---

Published in Transactions on Machine Learning Research (02/2025)
sensitive attributes. Similarly, DECAF (Van Breugel et al. 2021 , causally-aware GAN-based framework, incorporates structural causal models into the generator to ensure fairness, enabling inference-time debiasing and compatibility with multiple fairness definitions. However, these approaches do not effectively tackle the imbalance in sensitive classes within synthetic data, and diffusion-basee tabular data synthesis methods such as Kotelnikov el al. (0023 : Lee et al. (2023): Zhang el al. (2023) excel producing synthelic data of superior quality. Fair Class Balancing (Yan et al., 2020), an oversampling algorithm based on SMOTE, demonstrates impressive results but lacks flexibility in manipulating data distributions. As an interpolation- baseed method, it struggles with data diversity and is not well-suited for multi- generation. Recently, Frie4FFree (Sikder et al. 2024) introduced novel approach to fairness-aware data synthesis using data-free distillation, which trains & smaller student model to generate fair syuthetic data while preserving privacy and outperforming state-oftthe-ar hods in fairness, utility, and data quality.
2.3 Fair/Safe Diffusion Models
With impressive capabilities in generative modeling, diffusion models are a class of deep generative models that utilize a Markov process that starts from noise and ends with the target data distribution. Diffusion models have demonstrated superior performance over large language models in modeling structured data like images and tables, they are specifically designed to handle mixed-lype features and maintain complex inter-dependencies (Zhang et al. 2023)- Although diffusion models are widely studied, research on the fairness and safety of diffusion models is limited, and existing works (Schramowski et al., 2023 Friedrich ct al., 2023) mainly focus on text-to-image synthesis tasks. Friedrich et al. (2023) utilize sensitive content, such as sex and race, within text prompts to guide training diffusion models. Subsequently, during the sampling phase, a uniform distribution of sensitive attributes applied to generate images that are fair and do not exhibit preference towards privileged groups. Similarly, (Schramowski et al.] 2023) uses unsafe text, such as nudity and violence, to guide diffusion models and remove unsafe content in the sampling phase, In addition, this method applies some techniques to remove unsafe content. from synthetic images while keeping changes minimal. However, these fairness-aware diffusion models have not been adapted to tabular data synthesis, even though bias commonly exists in tabular datasets (Le Quy et al. 2022). This paper studies how to model mixed-type tabular data conditioning on labels and multiple sensitive attributes in latent space. Our mothod can generate balanced tabular data considering multiple sensitive attributes and subsequently achieve improved fairness scores without compromising the quality of the synthetic data.
3 Diffusion Models
Diffusion models, laking inspiration from thermodynamies, are probabilistic generative models that operate under the Markov assumption. Diffusion models involve two major components: forward process and reverse process. The forward process gradually transforms the given data Xp into noise XT passing through T steps of the diffusion kernel x(x))xx=1). In the reverse process, Xy is restored to the original data X by T steps of posterior estimator po px(x (xx) with trainable parameters 0. In this section, we explain the Gaussian diffusion kernel for contimuous features and the multinomial diffusion kernel for discrete features. Furthermore, we show how to optimize the parameters 0 of the posterior estimator considering mixed-type data with both continuous and discrete features.
3.1 Gaussian Diffusion Kernel
The Gaussian diffusion kernel used in the forward process for continuous features. After a series of Gaussian diffusion kernels with T timesteps, XT follows the standard Gaussian distribution (0. I). Specifically, with the values of Br predefined according to schedule such as linear, cosine, etc. (Chen 2023 the Gaussian diffusion kernel ((x((x(1) can be written as:
g(xx|xx-1) = // -x,
With the starting point Xy known, the posterior distribution the forward process can be calculated analytically follows,
(xx,-1 (x,, xn) = -x (x,, xo), 3.I
4

---

Published in Transactions on Machine Learning Research (02/2025)
1
Br
(x, xo)
(Xt
(1)
V(l.
vI
a2
B. 0i--1 Be
at
where a =1 Be, a = I ag. In the reverse process, posterior estimator with parameters 0 is used approximate P (x,) at each timestep given X,.
Po px(xx-1lx)) (xx(-11(xx)) E(x))
Ho et al. (2020) set the estimated covariance matrix (xx) as a diagonal matrix with constant values 3 or 3r, and calculate the estimated mean (x(x)) as follows,
M(x)) 1
3r ((x)))
(xt
(2)
Vau v1 at
where E(x:) is the estimated noise at time t.
3.2 Multinomial Diffusion Kernel
The multinomial diffusion kernel is applied in the forward process for discrete features with the same noise schedule 3r used by the Gaussian diffusion kernel. For discrete features, in the final timestep T, follows the discrete uniform distribution Uniform(A3) (K), where K is the number of categories. The multinomial diffusion kernel can be written as:
(xx/(xx11) = Cat(x: ((1 31)x;-1 + 3./K)
Similarly to the continuous case, the posterior distribution of the multinomial diffusion kernel has an ana- lytical solution.
q(x; X2, xo) Cat((x--11)) 10/ Σ 0n)
i = [axx + (1 (1- (1-00)
- + ar-1//K]
The posterior estimator for discrete features is preferably parameterized by the estimated starting point 2x(x,) as suggested by Hoogeboom et al. 2021
po (x (x = g(x: x0,,
(3)
3.3
Model Fitting
The parameters 0 of the posterior estimator are learned by minimizing the variational lower bound,
[(xo) = -Ey(xs) (Xgg))/((/(//x)xxxxxxxxx) - log po (xolxi)
I
(4)
where Kullback-Leibler divergence is denoted by D. The variational lower bound can be simplified for continuous variables as the mean squared error between true noise in Equation ① and estimated noise in Equation denoted by LG.
Le Eg(x) [lle {(x
For mixed-type data containing continuous features and C' categorical features, the total loss LT can be expressed as the summation of the Gaussian diffusion loss term LG and the average of the multinomial diffusion loss terms in Equation i for all categorical features:
CT = + Eise (())
To train latent diffusion models, lct 2, be the latent representation of x,, the estimated noise ((x,) for continuous features in Equation 2 can be reformulated as ε(z,). The estimated original input xx(x;) in Equation 3 can be reformulated (2,).
5

---

Published in Transactions on Machine Learning Research (02/2025)
3.4 Classifier- Free Guidance
Classifter-fre guidance (Ho & Salimans 2022 is mechanism used in diffusion models to condition the generation process on auxiliary information without requiring an external classifier. Below, we provide a mathematical overview of this method. Let 24 denote the noisy data at timestep & in the reverse diffusion process, and let с represent the conditional information (e.g., a class label). The reverse diffusion process aims to estimate the posterior p(zt_1/zt, c), which parameterized by a neural network eg. Classifier free guidance interpolates between conditional and unconditional estimates of the noise, as shown below,
e( c) = c0(x)) + (egee( c) - co(x)))
where (xx) is the unconditional noise estimate, c0 c) is the conditional noise estimate, and Wig is the guidance weight, controlling the influence of the conditional term. The term eo(2;, c) <0(Tz) represents the guidance signal derived from the condition By scaling this signal with Wg, the model adjusts the strength of conditioning during generation. Larger values of ul enforce stronger adherence to the condition, while smaller values reduce inffuence, yiclding outputs closer to the unconditional distribution.
For tasks involving multiple conditions, the guidance term be extended to support multivariate condi- tioning. Suppose have N conditions cı, , the guided noise estimate can be formulated as,
e(I;, C1, C,.,,, c0(2))
Wg. (Ft,
where ((a,, cz) = co (ze; G) - co (xt) represents the guidance signal for the i-th condition, and Wg. represents the i-th guidance weight. The overall guidance is compuled as the sum of these individual contributions across all conditions.
4 Methods
As explained in Section generative diffusion models for tabular data are prone to learning the sensitive group size disparity in training data. In this section, we describe the process of synthesizing balanced mixed-type tabular data considering sensitive attributes. We first introduce conditional generation given multivariate guidance while prescrving the synthesis quality. Then, we explain the architecture of the back- bone deep neural network used the posterior estimator in our method. Lastly, explain the procedure for sampling balanced data with consideration for sensitive attributes. The high-level structure of our method is shown in Figure
4.1 Multivariate Latent Guidance
To achieve conditional data generation with diffusion models, classifier-Iree guidance (Ho Salimans 2022)
is a simple but effective approach. Intuitively, the classifier-fres guidance method tries let the posterior estimator "know" the label of the data it models. In latent space, it uses neural network to receive 2, and condition to make estimations. We can denote the estimated noise given corresponding condition for innuus features as E(2,, c)2
E(Z,, - E(Ze) Wy (É((,, c) ε(Ze)
(5)
((2.c)
where ((z,, c) 0(zz, c) ((z.) is guidance of c and it scaled by guidance weight Wgg
Equation can be extended to support multivariate guidance. Especially in tabular synthesis, want to generate samples conditioned on labels and sensitive features In this case, can let our neural network take Zt, c, and s as inputs, resulting in:
c,,,,
c, = E(z,) wg(7(2,, 7(2,, c,8))
(6)

---

Published in Transactions on Machine Learning Research (02/2025)
Gaussian Diffusion
Xyrm
Conditioning Balanced Samplig
Preprocessing
MLP
Muttinomial Diftusion
Xail
E(x,-1
MLP
KV
KV
KV
KV
MLP
KV
Denoising Step
Cross Altention
Skip Connection
The diagram of our model architecture. In the forward process, the input data point x is pre- processed into numerical part Xnum and categorical part Xeat, and then passing through T steps of the diffusion kernel to gel XI, ,T. 21. process, the posterior estimator iteratively denoises noisy imput zr conditioning on an outcome с and sensitive attributes s()),.. ,s()) The estimated data point is Our model can generate fair synthetic data by leveraging sensitive guidance to ensure a balanced joint distribution of the target label and sensitive attributes.
Figure 1:
Zr is the latent represenlation of XI, Xy. In the reverse
Using the mechanism of multivariate classifier-free guidance introduced in Section 3.4 Equation (6) extends label-only conditioning in classifier-free guidance with multivariate feature-level conditioning. The term ((z, c) is the guidance term for the target label, representing the adjustment to the noisc estimate basod on the label condition. The term r(z,, c,s) the sensitive guidance term, incorporating adjustments based on the sensitive attribute s, and the label condition here used as reference to restrict the influence of the sensitive guidance, ensuring fairness without distorting the primary label distribution. Since the primary conditional guidance is the target label, incorporating sensitive guidance from multiple attributes should influence the synthetic data without significantly altering it compared to using only the target label as guidance. To achieve this, the sensitive guidance 7(z, c, is controlled clement-wiscly by "sccurity gate" pac, 8; A),
(Zz, c,s) = u(,, A){((.,ss) E(Z,))
s, ws, A) = s
where E(z., c) θ E(2,, <
c(c, 8;
= 0. otherwise
6 max(1, wzlē(ze, - s(l
where @ is element- wise subtraction, and w, is the sensitive guidance weight. The purpose of p(c, s; w., A) is keep the alterations caused by the sensitive guidance stcady and minimal. deactivates the sensitive guidance when the difference between the estimation conditioned on and the estimation conditioned on is larger than threshold X. Furthermore, sensitive guidance deactivated in the early phase of the diffusion process when is smaller than warm-up timestep d (a positive integer indicating the timestep at which sensitive guidance starts):
722, C, 0f
7

---

Published in Transactions on Machine Learning Research (02/2025)
Additionally, momentum term with momentum weight Wm is added to the sensitivity guidance to make guidance direction stable,
c, 8; Way A)(E( Wi, E((z)) ) + Wm
2t+1 3v, (1 8)%
where Mo 0 and B E 1). To include not only sensitive feature, but N sensitive features S {s(1), ... , we can extend the sensitive guidance as follows:
(2,,
Σ w. A)(((), E(2+))
By this approach, we model continuous features given labels and a set of sensitive features S. Similarly, the estimated starting point given and S for discrete features can be formulated as follows,
(o &o(2)) + #y (2( c) 72,, c, S))
(7)
with 7(4,, c) and 7(2,, S) parameterized by &n.
4.2 Backbone
In this rescarch, we model mixed-type tabular data in latent space following a similar setup in (Rombach let al. (022).. The tabular data are encoded into latent space and decoded back with multilayer perceptrons. We choose U-Net (Ronneberger et al., 2015) with transformers and Ro(2,, S). This choice is based on the hypothesis that the U-Net, augmented by the contextual understanding capabilities of transformers, can effectively manage the heterogeneous nature of tabular data. This integration leverages the U-Net's ability to capture spatial correlation and the transformers' strengths in sequence modeling, thereby offering a novel approach to latent space modeling of tabular data.
posterior estimator to predict E(Z,, c, S)
4.3 Balanced Sampling
After training diffuusion models to approximate the distribution of the training data conditioned on labels c and sensitive features S, we can generate samples from this conditional distribution and align them with any desired larget distribution by adjusting the conditioning variables. Specifically, during sampling, the denois- ing neural network utilizes guidance from the target label and sensitive attributes. The joint distribution of the target label and sensitive attributes can be customized. In our bias mitigation framework, we expect the synthetic data to preserve the same label distribution as the real data, while making the sensitive attributes nearly independent of the target label. To achieve this, we first compute the empirical label distribution from the real data and apply uniform categorical distributions for each sensitive feature. This results in a balanced joint distribution of the target label and sensitive features, which serves as input for the conditional generation in our diffusion model. Then our diffusion model iteratively denoises the Gaussian noise with label and sensitive guidance generate fair tabular data.
5 Experiments
In this section, we first introduce experimental datasets, data processing, baselines, and how we assess synthetic tabular data. Then we present computational results on experimental datasets. The training settings are presented in Appendix B
5.1 Datasets
We evaluate the effectiveness and fairness of our proposod method using three binary classification tabular datascts: Adult KKhaaii et al. 1996), Bank Marketing (Moro et al. (2014), and COMPAS (Larson et al. 2016). These datasets contain numerical and categorical features, including some sensitive attributes with
8

---

Published in Transactions on Machine Learning Research (02/2023)
observed class imbalances strong correlations to the target variable. We provide summary of the datasets, including their corresponding sensitive attributes in Table 1 and offer further details in Appendix A Common sensitive attributes such as sex and race can be found in Adult and COMPAS. As mentioned in (Rajabi & Garibay, 2022 the age group in the Bank Marketing dataset is considered sensitive, with individuals below 25 classified as "young" and those above 25 classified as "old". In our experiments, each data set is divided into training, validation, and test sets using a 50/225/25 split. Following the training of our diffusion models with multivariate guidance, we proceed generate synthetic data that adheres to the original label distribution while incorporating a uniform distribution for sensitive features.
Table 1: Experimental datasets.
Dataset
Train
Validation
Test
Sensitive
Target
Adult
22611
11305
Race
Income
Bank 22605
11303
11303
Age Sex & Race
Subscription
COMPAS
8322
4161
4161
Recidivism
5.2
Data Processing and Baselines
5.2.1 Data Processing
Following the data processing steps in Kotelnikov et al. (2023), we split the numerical and categorical features for cach dataset and pre-process them separately. Numerical features are converted through quantile transformation, a non-parametric technique transforming variables into a standard Gaussian distribution based on quantiles, thus normalizing the data and enhancing the robustness to outliers. In preparation for the Markov transition process the latent diffusion model, we encode categorical fcatures into one-hot vectors and concatenate the resulting vectors with normalized numerical features.
5.2.2 Basclines
To evaluate the effectiveness of our approach across different datasets, we perform a comparative analysis against state-of-the-art (SOTA) baseline models that have publicly available code for tabular data synthesis. Additionally, we compare our method with fair tabular generative models based on GAN and SMOTE. The specific baseline methods chosen for comparison detailed below.
Fair Class Balancing (FairCB) Yaan et al. 2020 a SMOTE-basod oversampling method adjust class distributions in the training data to address class imbalances.
TaN (FairTGAN) (Rajabi & Garibay
2022 : a fairness-aware GAN tailored for tabular data incorporates a fair discriminator to ensure equality in the joint probability distribution of sensitive features and labels.
CoDi [Le et al. 2023 a recent diffusion-based approach that combines two diffiusion models: one for continuous variables and another for discrete variables. These models are trained separately to effectively learn the distributions of their respective data types. However, they are also linked together by conditioning cach model's sample generation the other model's samples at cach line step, which enhances the learning of correlations between continuous and discrete variables. Additionally, to further strengthen the connection between the two models, contrastive learning is applied to both diffusion models. Negative samples are created by breaking the inter-correlation between the contimuous and discrete variable sets, thus reinforcing the models' ability to learn these relationships.
GReaT [Borisov et al. 2022): utilizes pre-trained transformer-decoder language models (LLMs) to generate synthetic tabular data through a two-stage process. In the first stage, transforms each row of the real tabular datasct into a textual representation by representing cach feature with its name and value in subject-pree prejicate-ojeect structure. The transformation of these features is then concatenated in random order to form the feature vector of the corresponding row. The random
9

---

Published in Transactions on Machine Learning Research (02/2025)
ordering enables the LLM to be fine-tuned on samples without depending on the sequence of features, allowing for arbitrary conditioning during tabular data generation. This textual representation of the tabular dataset is used to fine-tune the pre-trained LLM. In the second stage, the fine-tuned LLM generates syuthotic data by initializing with any combination of feature names or feature name- value pairs, allowing the model complete the remaining feature vector in its textual representation, which can then be converted back into tabular format.
SMOTE (Chawla et al. 2002 a robust interpolation-basel method that creates synthetic data points by combining a real data point with its k nearest neighbors from the dataset. In our appli- cation, we leverage SMOTE to generale additional tabular data samples, employing interpolation among existing samples with the same label. The implementation SMOTE is available in Lemaitre ct al. (2017).
STaSy (Kim et al. 2022 (core-baseds Tabular Data Synthesis (STaSy) method that adopts the score-based generative modeling approach. It introduces a self-paced learning method that begins by training on simpler records with a denoising score matching loss below a certain threshold. As training progresses and the model becomes more robust, progressively more challenging records are included in the training. Additionally, STaSy includes a fino-tuning approach that adjusts the model parameters by iteratively retraining it using the log probabilities of the generated samples.
TabDDPM (Koelnikov et al. 2023 :: a recent iiffusion-based model that has demonstrated to surpass existing methods for synthesizing tabular data. Leveraging multilayer perceptrons as its backbone, TabDDPM excels in learning unbalanced distributions the training data.
TabSyn (Zhang al. 2023 SOTA method that utilizes latent diffusion model to generate labular data containing both numerical and categorical features. First, VAE model, specifically designed for tabular-structuree data, converts raw tabular data with both numerical and categorical features into embeddings in a regularized latent space that has a distribution close to the standard normal distribution. Following this, a score-based diffusion model is trained in the embedding space to capture the distribution of these latent embeddings. The diffusion model adds Gaussian noise that increases linearly over time in the forward process, which minimizes approximation errors in the reverse process, thus allowing for synthetic data to be generated in significantly fewer reverse steps.
5.3 Assessing Synthetic Tabular Data
There are two primary approaches to evaluating synthetic tabular data. One approach is model-based method that involves training machine learning models on synthetic tabular data and evaluating the trained models on validation data extracted from the original dataset. The other approach is a data-based methot that operates independently of machine learning models and directly compares synthetic and real tabular data.
5.3.1 Basa-Based Evaluation
We evaluate synthetic tabular data by directly comparing it with real data, with a focus on column-wise density estimation and pair-wise column correlations. To address privacy concerns when sharing tabular data publicly, synthetic data should not replicate the original data. As suggested by (Zhang al. 2023). we compute "distance to the closest record" (DCR) scores ensure this. Furthermore, when assessing the fairness of synthetic tabular data using data-based methods, analyze the class imbalances within sensitive features like sex race, well as the joint distribution with the target label.
5.3.2 Model-Based Evaluation
The model-basod evaluation process for synthetic tabular data in classification tasks involves the following steps: first, training a generative model on the original training set; next, generating synthetic data using the trained generative model; then, training classifiers the synthetic data; and finally, evaluating these
10

---

Published in Transactions on Machine Learning Research (02/2025)
classifiers the original test data. Specifically, when assessing model accuracy the original test data, it is referred to as machine learning efficiency as used in (Choi et al. 2017).
Beyond accuracy assessments, the evaluation of synthetic data can extend to fairness scores of classifiers, providing insights into the fairness of the generated data. We evaluate the fairness of machine learning models trained on the synthetic data, focusing on two key aspects: equal allocation and equal performance (Agarwal et al. 2018
Equal allocation, fundamental principle in model-based fairness evaluations, entails the idea that a model should distribute resources or opportunities proportionally among different groups, irre- spective of their affiliation with any privileged group. Equal allocation is quantified using the demographic parity ratio (DPR). This ratio reveals the extent of the imbalance by comparing the lowest and highest selection rates (the proportion of examples predicted positive) between groups.
Equal performance is grounded in the principle that fair model should exhibit consistent perfor- mance across all groups. This entails maintaining the same precision level for each group. The equalized odds ratio (EOR), which represents the smaller of two ratios comparing true and false positive rates between groups, serves as a metric for assessing performance fairness in this context.
Both the demographic parity ratio and equalized odds ratio are within the range [0, 1]. The demographic parity ratio highlights the importance of distributing resources equally among different groups, whereas the equalized odds ratio prioritizes impartial decision-maker making within each specific group. Higher values in either metric indicate progress toward fairness. In our experiments, when evaluating the fairness scores of classifiers as shown in Table 4 we evaluate the fairness scores of classifiers by selecting sex as the sensitive attribute for the Adult and COMPAS datasets, and age group for the Bank dat aset.
5.4 Computational Results
This section presents the computational results of our evaluation of synthetic tabular data generated with three diffcrent random seeds. We trained the SOTA machine learning model for tabular data, CatBoost, three different sets of synthetic dala, each generated with unique random seed.
5.4.1 Fidelity and Diversity of Synthetic Data
Table 2: The values of error rates of column-vir wise density estimation and pair-wise column correlations.
||
Adult
Bank
COMPAS
Mean
Adult
Bank
COMPAS
Mean
Density (4)
%
Correlation (1)
%
CoDi
.145
.154 0000
.2052.001
16.8%
.495
.3444.001
.550
46.3%
GReaT
.077 000
.102 001
.093 001
9.1%
.208 016
.213
.165 016
19.5%
SMOTE
.025+.000
.020
.021 .0214.001
2.2%
.0544.
.042+.005
.047
4.8%
ST&Sy
..02
.182+..00 ooe
.1084.001
13.1%
.103 000
.211+.001
.138+.00r
2.8.+.000
17.4%
TM
10371-001
.028 oon
.057
4.1%
.055
.0521-001
.090.1
6.6%
TabSyn
00101.000
.009 ooo
.027 0271.001
1.5%
.036
.033 002
.0544 001
4. 11%
FairCB
07664.00
.006 .000
.039...00
6.0%
.125 0o0
.1112.000
.07444 o08
10.3%
FaiTGAN
.0341.c0
.030
.0551..01
4.0%
.080
.0531.000
.087
7.3%
Ours
122
.121 0000
.0092.001
11.9%
.2012.000
.1742.005
.1744 003
18.3%
Table and Table 3 show that method remains competitive with SOTA models in terms of column-wise density estimation, pair-wisc column correlations, and machine learning efficiency, even after incorporating a uniformly distributed direction for sensitive features during the sampling phase. Across the three exper- imental datasets, our model achieved an average AUC of 84.7%, which is only 1.3% lower than TabSyn, the SOTA deep learning tabular synthesis method. Although altering the original sensitive distribution is anticipated to reduce AUC, the results demonstrate that our method effectively preserves high fidelity in the generated synthetic data. While SMOTE and FairCB excel in fidelity, their low DCR scores suggest
11

---

Published in Transactions on Machine Learning Research (02/2025)
Table 3: The values of machine learning efficiency with the best classifier for each dataset and DCR scores.
Adult
Bank
COMPAS
Mean
Adult
Bank
COMPAS
Mean
AUC (1)
%
DCR Scores (Ideally Around .500)
%
Real
.28+.000
9364+.00
8800+000
89.1%
CoDi
8584.001
8264 014
678+.000
78.7%
.331+.00 008
.484.001
36.0%
GRea'T
101
6888 1.
.7172.008
76.9%
.320
.348..00
.377
34.8%
SMOTE
.9142.000 000
.928 001
202
87.3%
604
.265..001
.273 00a
STaSy
886+..00
8955
.728+.013
83.6%
.344
.355+.002
.362.
35.0%
TsbDDPM
+07
.974
.745+.001
85,
.399 000
.350 2022
.307 +
35.2%
TabSyn
.911..00
.919 000
.749
86.0%
339
.351
.367
35.2%
FairCB
9154.001
.907 002
2.71
86.4%
.054 000
.031
.0124 001
3.2%
PaiTTGAN
8814.000
8634 0006
.705+.002
81.6%
3384.002
.318+.004
.374 0008
35.7%
Ours
.833 002
.944 002
.03
84.7%
.344 601
350
.370 004
35.5%
that they closely mimic the real data, which is expected given their direct interpolation of the original data points.
5.4.2
Fairness Scores in Classification Tasks
Table 4: The values of DPR and EOR with best classifier for each dataset.
|| Adult
Bank
COMPAS
Mean
Adult
Bank
COMPAS
Mean
||
DPR (t)
%
EOR (1)
%
Real
||
4.09
.402 016
.6752.00 .000
46.2%
.993 000
.367..024
.645
40.2%
CoDi
.293
1199
.855
44.6%
.247
.1722.063
.857
42.5%
GReaT
2.1
.572..250
.624=.066 066
48.2%
.155 028
.380
.543 076
35.9%
SMOTE
.321
.405+.028
.648+.021
45.8%
.254-.00
.381+.026
.589
40.8%
STaSy
.261 1.045
.436 .92
38. 8%
.182 069
..51
.433
35.5%
TabDDPM
.61 +.00
.337+.020
.558+.00
38.5%
.156-
.334+.028
.540.
34.3%
TabSyn
281 4.017
.336
.697 2.970.000
13.8 83.8%
.1782.019
3172.003
.664
38.6%
FairCB
2861 002
.7194.005
.675 006
56.0%
.192 0oa
.8011.016
.638 021
PaiTTGAN
5544.000
.384
.448
44.7%
.697
.158
.392
41.6%
Ours
3431.026
.710 057
1800g.0m
68.4%
[667 000
.64191.00
.8251.113
71.4%
As shown in Table our method significantly outperforms all baseline models, achieving mean DPR of 68.4% and mean EOR of 71.4%. In comparison, state-of-the-art tabular generative models and Fair TGAN fall below 50% for both fairness metrics, while FairCB remains below 60% for both. From Table 3 our method achieves an average AUC of 84.7 %, which is higher than FarTTGN and only 1.6 % lower than FairCB. By jointly optimizing performance and fairness, our method presents a more balanced trade-off than existing approaches. Morcover, in Appendix E.1 we demonstrate that simply overwriting the distribution of sensitive attributes is insufficient to miligate bias in downstream classification lasks and can lead to a decrease in classification performance.
5.4.3 Sensitive Feature Distributions
In addition to evaluating the fairness of machine learning models, we also examine the class distribution of sensitive attributes in senthetic data. To achieve this, we use stacked bar plots to visualize the contingency tables for sensitive fcatures and the target label. We further compare the distribution of sensitive attributes between real and synthetic data using bar plots.
We present the distribution of sensitive attributes versus the target label on the Adult dataset using method in Figure 2 The synthetic data generated by our approach demonstrates balanced joint distribution between ivnsitive features and the target label, effectively reducing the disparities observed in the original data. Additionally, we provide plots of the real data distribution and synthetic distributions generated by other methods in Appendix D Note that we only visualize the synthetic data generated by our method, FairTGAN, and FairCB, which are three fairness-aware generative models, because we assume that the other baseline models replicate the real data distribution. In comparison, our method effectively produces
12

---

Published in Transactions on Machine Learning Research (02/2025)
(Ours) sensitive attributes vs target label the Adult dataset
Sex
Race
Female Male
Amer-indian-
Asian-Pac-islander
0.6
0,6
Percentage
Percentage
Black
Other
0.4
0.4
White
0.2
0.2
0.0
0.0
>50K
<<50K
>50K
Class
Class
Figure 2: The distribution of sensitive attributos across different target label values the Adult dataset using our method.
balanced joint distribution of sensitive attributes and target labels than the FaiTTGAN, FairCB, and the real distribution.
Sensitive Attribute Distribution in Real and Synthetic Data with Our Method
0.861
0.971
Age: Group
Purcentage
0.671
Race
Parcoitage
Percentage
0.544
Race
0.329
0,327
0.29
0.093
0.027
0.008
0.-
0.179
0.076
0.044
0.005
0.003
0.0
25.
0.527
Durs
2.4
Parcentage
044
Porcntage
32:
0.190
0.190
0.198
0.66 0.170
0.1
0.1
Num
mi/k
Bick
bilen
0.
06
0.0
Micon
Figure Comparison the real versus synthetic distribution of sensitive altributes across datasets.
5.4.4 Performance- Ferformance-Fairness Trade-Off
Balancing fairness and predictive accuracy is a key challenge in synthetic data generation, particularly when fairness improvements might impact important outcomes. This section explores how adjustments in fairness levels influence both performance and fairness metrics in the generated data. Our method provides way to control this trade-off, allows practitioners to find balance that meets their specific needs, whether they prioritize fairness, accuracy, or a compromise between the two.
To analyze the trade-off between fairness and accuracy, we define balancing level i ranging from 0 10. This level quantifies how much we adjust the original joint distribution of the target and sensitive attributes to make it more uniform.
Suppose have N sensitive attributes, each attribute with C unique values, where i ranges from 1 to N. This results in K combinations of values where K = C; x ... x Cy. Under the same targe label, cach combination has a probability th where is one of the K combinations. For each combination, we calculate an offset dy = y---yp yk the difference between the average probability ý = Σ ye/ y and the current probability Ve- The offsct de represents how much the current joint probability deviation ys for a combination of sensitive attributes and the target label deviate from the average probability ÿ. By identifying these deviations, we measure how far the joint distribution is from uniform distribution, which is often desired for achieving
13

---

halanced are Published in Transactions on Machine Learning Research (02/2025)
fairness. For balancing level E [0, the balancing ratio is i/10, and the adjusted values yk calculated as:
yk yk + dg 10
balanced
The balanced values yk and the target label after applying a fairness transformation. The balancing process ensures that y ballanedd systematically reduces disparities in the joint distribution of sensitive attributes and target labels, moving it closer to uniformity as the balancing level increases. By choosing appropriate balancing level, the method allows for a practical balance between performance and fairness.
shalanced represent the adjusted probabilities for each combination sensitive attributes
The results presented in the previous sections are based on a balancing level of 10, which achieves a nearly uniform joint distribution of the larget and sensitive altributes. We conducted experiments to assess how different balancing levels affect the performance-faine fairness trade-off. We evaluated metrics such as AUC, DPR, and EOR, with DPR and EOR avcraged across all sensitive attributes. We computed a composito score a weighted sum of these metrics, with weights of 0.5 for AUC, 0.25 for DPR, and 0.25 for EOR. Figure 4 illustrates the trade-off between fairness and performance across different balancing levels. The optimal composite scores for the Adult and Bank datasets are achieved at balancing level of 10, indicating uniform distribution. For the COMPAS dataset, the best score is at level 9, representing nearly uniform distribution. Practitioners can adjust the metric weights to suit specific application needs or establish thresholds for individual metrics based on their requirements.
Performance-Faiiness Trade-Off for Experimental Datasets
CONPAS
Marketing
0.7
0.5
0.6
0.5
0.4
0.-
2111
DTR
0.3
2011
10
Figure Trade-off between performance and fairness across balancing levels for experimental datasets. SCORE was computed as a weighted sum of these metrics, with weights of 0.5 for AUC, 0.25 for DPR, and 0.25 for EOR. The best composite scores for the Adult and Bank datasets achieved at balancing level of 10, while the COMPAS aset achieves the best score at level 9.
6 Limitations and Discussion
Our proposed method is designed to generate balanced synthetic tabular data while considering sensitive attributes. However, it requires sensitive features to be specified in advance, which can be challenging large-scale enterprise datascts with thousands of features. Furthermore, we could extend our testing datasets with more than two sensitive altributes and inbalanced (arget distributions.
U-Net with attention layers the backbone neural network for the posterior estimator in the diffusion framework, but this approach is more timo-consuming compared to TabSyn. As the number of sensitive attributes increases, there are more hyperparaneters to tune, which makes fine- tuning more computationally demanding, especially with the long sampling time. In the future, we aim to explore methods to roduce computational costs. We believe the main bottlenecks computational costs are due the long diffusion timesteps and the quadratic complexity of attention modules. Possible solutions include reducing diffusion timesteps via acceleration methods such Flow-DPM-Sollvr Xie et al. (0244) and
In terms of efficiency,
14

---

Published in Transactions on Machine Learning Research (02/2025)
refining the backbone, for instance, by employing linear attention Katharopoulos et al 2020 or by using pre-trained autoencoder to embed tabular data into lower-dimensional latent space.
Lastly, our proposed method is designed to condition on specified group labels, which limits its applicability in scenarios where demographic information unavailable or restricted due to privacy concerns. Future iterations of the model could integrate privacy-preserving techniques, such as synthetic attribute imputation, to enable fairness-aware data generation without requiring explicit sensitive attribute disclosure.
On the other hand, our proposed method generates fair data with balanced joint distribution of the target label and multiple sensitive attributes, achieving higher fairness scores than baseline methods. Additionally, the model shows strong potential for extending to multimodal data synthesis. If we can improve the efficiency of our model and successfully adapt it to multinodal scenarios, we believe it could be widely applied to address fairness issues in fields such as healthcare, finance, and beyond.
7 Conclusion
In this work, we propose a novel diffusion model framework for mixed-type tabular data conditioned on both outcome and sensitive feature variables. Our approach leverages a multivariate guidance mechanism and performs balanced sampling considering sensitive features while ensuring a fair representation of the generated data. Extensive experiments real-world datasets containing sensitive demographics demonstrate that our model achieves competitive performance and superior fairness compared to existing baselines.
Impact Statements
The objective of this study is to make progress in the area of fair machine learning. Tabular datasets sometimes contain inherent bias, such imbalanced distributions in sensitive attributes. Training machine learning models on biased datasets may result in decisions that could negatively affect minority groups. By mitigating biases present in tabular datasets through the generation of equitable synthetic data, our approach contributes to fostering equitable decision decision-making processes across industries such as finance, healthcare, and employment. Morcover, in instances where sharing datasots becomes necessary, the utilization of fair synthetic data ensures the preservation of user privacy and avoids causing emotional distress to minority groups. An adverse possibility is that bad individuals could manipulate distributions in sensitive attributes to generate biased (even stronger than original) data to harm minority groups. Additionally, the widespread release of synthetic data can diminish the quality of data sources available on the internet. Repeatedly training generative models on synthetic data in a self-consuming loop can lead to a decline in the quality of data synthesis, as discussed in (Alemohammad et al. 2023
References
Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. A reductions approach to fair classification. In International Conference on Machine Learning, pp. 60 69. PMLR, 2018
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: A next- generation hyperparameter optimization framework. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2623 2631, 2019.
Sina Alemohammad, Josue Casco-Rodriguee Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaci, Daniel TeJeune, Ali Siahkoohi, and Richard G Baraniuk. Self-consuming generative models go MAD. arXiv preprint Xiv:22307 01150, 2023.
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg. Structured denoising diffusion models in discrete state-spaces. Advances in Newral Information Processing Systems, 34:17981-18 17993, 2021.
Vadim Borisov, Kathrin Seßler, Tobias Leemann, Martin Pawelczyk, and Gjergji Kasnoci. Language models realistic tabular data generators. arXie preprint arXi:2221000620 2022.
15

---

Published in Transactions on Machine Learning Research (02/2025)
Flavio Calmon, Dennis Wei, Bhamukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varsh- ney. Optimized pre-processing for discrimination prevention. Aduances in Newral Information Processing Systems, 30, 2017.
Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357, 2002.
Ting Chen. On the importance of noise scheduling for diffusion models. arXiv preprint orXiv:2301.10912 2023.
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F Stewart, and Jimeng Sun. Generat- ing multi-label discrete patient records using generative adversarial networks. In Machine Learning for Healthcare Conference, pp. 286-305 PMLR, 2017.
Prafulla Dhariwal and Alexander Nichol. Diffusion models beat GANs on image synthesis. Advunces in Newral Information Processing Systems, 34:8780-8794, 2021.
Felix Friedrich, Patrick Schramowski, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Sasha Luccioni, and Kristian Kersting- Fair diffusion: Instructing text-to-image generation models on fairness. arXiv preprint ar 2rXi::2302.1055 2023.
Moritz Hardt, Eric Price, and Nati Srebro. Equality of opportunity in supervised learning. Advances in Neurul Information Processing Systems, 29, 2016.
Huan He, Shifan Zhao, Yuanzhe Xi, and Joyce C Ho. MedDiff: Generating electronic health records using accelerated denoising diffusion model. arXic preprint arXi:22022.0 04355, 2023.
Jonathan Ho and Tim Salimans. Classifier-fres diffusion guidance. arXiv preprint arXtc:2207.1550 2022.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6857 2020.
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling. Argmax flows and multinomial diffusion: Learning categorical distributions. Advances in Neural Information Processing Systems, 14:12454-12463 2021.
Sérgio Jesus, José Pombal, Duarte Alves, André Cruz, Pedro Saleiro, Rita Ribeiro, João Gama, and Pedro Bizarro. Turning the tables: Biased, imbalanced, dynamic tabular datasets for ML evaluation. Advances in Neurol Information Processing Systems, 55:33563-33575, 2022.
Faisal Kamiran and Toon Calders. Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems, 33(1):1-33 33, 2012.
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma. Fairness-aware learning through regularization approach. In IEEE International Conference on Data Mining Workshops, pp. 643-650. IEEE, 2011.
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. Vairness-aware classifier with prejudice remover regularizer. In Machine Learning and Knowledge Discovery in Databases: European Conference, pp. 50. Springer, 2012.
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. Trausformers are RNNs: Fast autoregressive transformers with linear attention. In International Conference on Machine Learning, pp. 5156-5165. PMLR, 2020.
Jayoung Kim, Chaejeong Lee, and Noseong Park. STaSy: Score-based tabular data synthesis. arXiv preprint wwXiv:2210.0401.. 2022.
Ron Kohavi et al. Scaling up the accuracy of naive-bayes classifiers: decision-treo hybrid. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume 96, pp. 202 207, 1996.
16

---

Published in Transactions on Machine Learning Research (02/2025)
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. DiffWave: A versatile diffusion model for audio synthesis. Xiv preprint or arXiv:2009.09701 2020.
Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, and Artem Babenko. TabDDPM: Modelling tabular data with diffusion models. In International Conference on Machine Learning, pp. 17564-17579. PMT.R, 2023.
Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin. How we ana- lyzed the COMPAS recidivism algorithm. How-we-i analyzed compas- aagormitlgggitmmmmmm 2016. Accessed: 2024-12-22.
https://wwwt /wwp./ propublica org/article
Tai Le Quy, Arjun Roy, Vasileios losifidis, Wenbin Zhang, and Eirini Ntoutsi. A survey on datasets for aware machine learning. Wiley Interdisciplnnary Reviews: Data Mining and Knowledge Discovery, e2(3)=e1452, 2022.
Chaejeong Lee, Jayoung Kim, and Noseong Park. CoDi: Co-evolving contrastive diffusson models for mixed- type tabular synthesis. In International Conference on Machine Learning, pp. 18940-119550 PMLR, 2023.
Guillaume Lemaître, Fernando Nogueira, and Christos K Aridas. Imbalanced-learm: A Python toolbox to tackle the curse of imbalanced datasets in machine learning. Journal of Machine Learning Research, 18 (17):1 5, 2017.
Yan Li, Xinjiang Lu, Yaging Wang, and Dejing Dou. Generative time series forecasting with diffusion, denoise, and disentanglement. Advances in Neural Information Processing Systems, 35:23009-23022, 2022.
Chao Ma, Sebastian Tschiatschek, Richard Turner, José Miguel Hernández-Lobato. and Cheng Zhang, VAEM: a deep generative model for heterogencous mixed type data. Advances in Neural Information Processing Systems, 33:11237-11247, 2020.
François Mazé and Faez Ahmed. Diffusion models beat GANs on topology optimization. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023.
Ninarch Mchrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey bias and fairness in machine learning. ACM Compuling Surveys, 54(6):1-33 35, 2021.
Sérgio Moro, Paulo Cortez, and Paulo Rita. A data-driven approach to predict the success of bank telemar- keting. Decision Support Systems, 62:22-30 31, 2014.
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. arxiv preprint arXiv:211 10741, 2021.
Dana Pessach and Erez Shmueli. A review on fairness in machine learning. ACM Compuling Surveys, 55 (3):1-44 2022.
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. On fairness and cali- bration. Advances in Neural Information Processing Systems, 30, 2017.
Amirarsalan Rajabi and Ozlem Ozmen Garibay. TabFairGAN: Fair tabular data generation with generative adversarial networks. Machine Learning and Knowledge Extraction, 4(2):488-50) 501, 2022.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with CLIP latents. arXie preprint orXiv:2204.0612 1(2):3, 2022.
Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf. Autoregressive denoising diffusion models for multivariate probabilistic time scries forccasting. In International Conference on Machine Learning, pp. 8857 8868. PMLR, 2021.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esscr, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684-10695, 2022.
17

---

Published in Transactions on Machine Learning Research (02/2025)
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Compuling and Compuler-Assislen Intervention, pp. 234-241. Springer, 2015.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala. Li, Jay Whang, Emily I. Denton; Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorcalistic text- to-image dilfusion models with deep language understanding. Advances in Newral Information Processing Systems, 35:36479 -6494,, 2022.
Timur Sattarov, Marco Schreyer, and Damian Borth. FinDiff: Diffusion models for financial tabular data generation. In Proceedings of the ACM International Conference on Al in Finance, pp. 64-72, 2023.
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting. Safe latent diffusion: Miti- gating inappropriaee degencration in diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pultern Recognition, pp. 22522 22531, 2023.
Md Fahim Sikder, Daniel de Leng, and Fredrik Heintz. Fair4Free: Generating high-fidelity fair synthetic samples using data free distillation. arXiv preprint arXiv:2410.01 014014 2024.
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathaa,, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pp. 2265. PMLR, 2015.
Michal Stypulkowski, Konstantinos Vougionkas, Sen He, Maciej Zieba, Stavros Petridis, and Maja Pantic. Diffused heads: Diffusion models beat GANs on talking-face generation. arXiv preprint or arXiv:2301.03390 2023.
Boris Van Breugel, Trent Kyono, Jeroen Berrevoets, and Mihacla Van der Schaar. DECAF: Generating fair synthetic data using causally-aware generative networks. Advances in Newral Information Processing Systems, 34:22221 1222233, 2021.
Faze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, et al. SANA: Efficient high-rosolution image synthesis with lincar diffusion transformers. arXiv preprinl ar Xiv:22 10629, 2024.
Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu. FairGAN: Fairnoss-awaro generative adversarial networks. In IEEE International Conference on Big Data, pp. 570 575. IEEE, 2018
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni. Modeling tabular data using conditional GAN. Advances in Neural Information Processing Systems, 32, 2019.
Shen Yan, Hsien-te Kao, and Emilio Ferrara. Fair class balancing: Enhancing model fairness without observing sensitive attributes. In Proceedings of the ACM International Conference Information & Knowledge Managementt pp. 1715 1724, 2020.
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi. Fairness con- straints: Mechanisms for fair classification. In Artificial Intelligecce and Statistics, pp. 962 970. PMLR, 2017.
Khadija Zanna, Kusha Sridhar, Han Yu, and Akane Sano. Bias reducing multitask learning on mental health prediction. In International Conference on Affective Computing and Intelligent Interaction, pp. 1 8. IEEE, 2022.
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. Mitigating unwanted biases with adversarial learning. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 335-340, 2018.
Hengrui Zhang, Jiani Zhang, Balasubramaniam Srinivasan, Zhengyuan Shen, Xiao Qin, Christos Faloutsos, Huzefa Rangwala, and George Karypis. dixed-type tabular data synthesis with score-based diffusion in latent space. arXiv preprint arXio:2310.0000 09656, 2023.
18

---

Published in Transactions on Machine Learning Research (02/2025)
A Details about Datasets
A.1 Adult
The Adult dataset (Kohaii et al.) 1996) is widely used benchmark for exploring fairness and bias in machine learning. It contains 48842 data points, Each data point has 14 attributes and binary target variable indicating whether an individual carns over fifty thousand dollars annually. The datasct encompasses employment, education, and demographic information, and sensitive features are sex and race. Download it from OpenML
A.2 Bank Marketing
The Bank Marketing dataset (Moro et al. 2014) is collected from direct marketing campaigns Portuguese banking institution. Comprising 45211 instances with 16 features, the predicted outcome binary variable indicating whether a client subscribed to a term deposit. Demographic, economic, and past marketing campaign data are included in features, and marital status is sensitive. Download it from OpenML
A.3 COMPAS
The COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) dataset Larson al. 2016 is derived from risk assessment tool used in the criminal justice system to evaluate the likelihood of recidivism among criminal defendants. It includes data on individuals arrested in Broward County, Florida, with 8 features and a binary outcome indicating whether the individual was predicted to reoffend. Features encompass demographic information, criminal history, and COMPAS risk scores, with sex and race being sensitive attributes. Download it from OpenML
Reproducibility
We performed our experiments on Ubuntu 20.04.2 LTS, utilizing Python version 3.10.14. Our framework of choice was Py Porch 2.3.0, with CUDA version 12.1, and we ran our computations on an NVIDIA GeForce RTX 3090. The hyperparameters we used for our method are listed as follows. The variable names can be found in our attached code. We tune the hyperparameters using Optuna Akiba et al. (0199 to optimize the validation AUC.
Table 5: The values of hyperparameters for our method.
Notation Variable
Meaning
Value/Search Space
batch_sias
Batch size
256
varmup_stepe
Warm-up timestep
0
Wa
cond seight
Sensitive guidance weight
1.0
cond_guid_ threshold
Threshold of the "security gate"
1.0
6'
cond nonentonn wei
Momentum weight Momentum correction factor
0.5
cond, ____nnmeetu,,_.m beta
0.5
overall_ _alll_gui__eeee weight tinesteps
Guidance weight
1.0
Number of timesteps the diffusion process
{100, 1000
n_epoche
Number of training epochs
[100, 1000)
Learning rate
[0.00001, 0.003]
For deep learning- based methods, we use the original hyperparameter settings provided in their respective implementations and mainly fine-tune the number epochs, learning rate, and number of diffusion timesteps (if applicable) to optimize performance. For SMOTE-basod mothods, we adjust the number of nearest neighbors to achieve better results. We have detailed the hyperparaneter search spaces for all bascline methods in Table 0 The hyperparameter search spaces the same for all datasets.
19

---

Published in Transactions on Machine Learning Research (02/2025)
Table 6: The values of hyperparameters for baseline methods.
Method
Variable
Meaning
Value/ SSarchh Space
CoDi
n_tinestepa
caal___poch__boe both
Number timesteps in the diffusion process
Number of training opachs
(100,500, 1000, 3000)
[100, 1000)
batch_siza
Batch size
(4,8) (4 (6, 10, 20)
GReaT
n_epocha
Number of training epochs
SMOTE
Number
neighhors interpolation
(2,
Learning rate
[0.00001, 0.003]
STaSy
AL epoche
Number training epochs
(100, 500, 1000)
Learning
[0.0000 0.003]
TabDDPM
nLepoche nun_ tinestepe
Number of training epochs
(100, 500, 1000} (100, 1000]
Number timesteps in the diffusion process
TabSyn
1r
Learning rate
[0.001, 0.002] (4000)
eeeoccee
Number training epochs
FairCB Xnn
Number nearest neighbors interpolation
(2, 21)
1r
Learning rate
[0.00001, 1, 0.003 (100, 500, 1000} (100, 500}
FaiTTGN
nLepoche
Number of training epochs Number of fairues-related training epochs
fair_epochs
C
Efficiency
Table provides summary of the number of parameters, training time, and sampling time for various tabular data synthesis methods. Our proposed method is moderately sized, with 482,545 paramcters, which is larger than TabDDPM but siguificantly smaller than most deep learning-based models, except FairTGAN. In terms of training efficiency, our method achieves a training time of 11.8 seconds per epoch, which is comparable CoDi (10.5 scconds per epoch). However, the sampling time signíficant limitation, as it takes approximately 1,200 seconds generate a synthetic dataset of the same size as the training data, making it substantially slower than all other baseline methods.
Table Comparison of training and sampling efficiency across methods
Method
Number Parameters
Training Time (s/epoch)
Sampling Time (s)
CoDi
2206711
10.5
9.1
GiteaT
81912576
SMOTE
51.1
17.0
ST&Sy
10653326
25.1
TabDDPM
109672
6.0
118.9
TabSyn
10575912
0.6
2.7
FairCB
64.3
5.4
FarTGGNN
45368
1.9
0.3
Ours
482545
11.8
1213.9
20

---

Published in Transactions on Machine Learning Research (02/2025)
D
Stacked Bar Plots of Contingency Tables
D.1 Adult
(Ours) sensitive attributes vs target label the Adult dataset
Sex
Race
Female Male
Amer-Indian-Eskimo
Aslan-Pac-lslander
0.5
0.6
Black
Percentage
Percentage
Other
White
0.2
0.2
0.0
0.0
50K
>50K
<<50K
>50K
Class
Class
(Real) sensitive attributes vs target label the Adult dataset
Race
Amer-Indian-Eskee
Pac-lslander
0.6
0.6
Percentage
Percentage
Other
0.4
0.4
White
0.0
0.0
50K
>50K
<<50K
>50K
Class
Class
(FairCB) sensitive attributes target label on the Adult dataset
Sex
Race
0.5
0.5
Female Male
Amer-Indian Eskimo
Asian-Pac-Isiande
Percentage
Percentage
Black
0.3
0.3
Other
White
0.2
0.1
0.1
0.0
0.0
<=50K
>5KK
<=5
>50K
(FairTGAN) sensitive attributes vs target label on the Adult dataset
Sex
Race
Female Male
Amer-Indian-Eskimo
0.6
Aslan-Pac-Islander
Percentage
Percentage
Black
Other
0.4
0.4
White
0.0
0.0
<<50K
>50K
<=50K
>50K
Class
Class
Figure Comparison of models the Adult dataset.
21

---

Published in Transactions on Machine Learning Research (02/2025)
D.2 Bank Marketing
(Ours) sensitive attributes vs target label the Bank dataset
Age-group
0.8
young
Percenage
0.6
yes
(Real) sensitive attributes target label on the Bank dataset
Age-group
young
Percentage
0.6
0.4
no
yes
(FairCB) sensitive attributes vs target label on the Bank dataset
Age-group
0.5
0.4
Percentge
0.3
0.2
old
0.0
no
yes
(FairTGAN) sensitive attributes vs target label the Bank dataset
Age-group
0.B
young
Percenage
0.6
0.0
yes
Figure 6: Comparison of models on the Bank Marketing dataset.
22

---

Published in Transactions on Machine Learning Research (02/2025)
D.3 COMPAS
(Ours) sensitive attributes vs target label on the COMPAS dataset
Sex
Race
0.5
Female Male
0.5
African-Ammrriann
Asian
0.4
0.4
Percentage
Percentage
Caucasian
0.3
0.3
Hispanic
Native American
0.2
0.2
Other
0.1-
0.0
0.0
No
Yes
No
Yes
1s_ is_recid
isrrecid
(Real) sensitive attributes vs target label the COMPAS dataset
0.5
Female Male
0.5
0.4
0.4
Percentage
Percentage
African-American
Asian
0.2
0.2
Caucasian
Hispanic
0.1
Native American
Other
0.0
0.0
No
Yes
No
Yes
Is_ recid
Is recid
(FairCB) sensitive attributes target label on the COMPAS dataset
Sex
Race
0.5
Female Male
0.5
Percentage
Percentage
African-American
0.3
0.3
Hispanic
0.1
0.1
American
Other
No
Yes
No
Yes
Is_recid
(FairTGAN) sensitive attributes vs target label on the COMPAS dataset
Sex
Race
Female Male
0.5
0.4
Percentage
Percentage
AferianAmmrrcan
0.3
0.3
Asian
Caucasian
Hispanic
0.1
0.1
Native American
Other
0.0
0.0
No
Yes
No
Yes
Is_ re_rd
1s_recid
Figure Comparison models on the COMPAS datasct.
23

---

Published in Transactions on Machine Learning Research (02/2025)
E Additional Numerical Results
E.1 Performance and Fairness Metrics with Sensitive Features Replaced by Uniform Distribution
We present performance and fairness of CatBoost trained on datasets with sensitive attribute replaced by uniform distribution in Table andd Table respectively.
Table 8: The values of machine learning efficiency with the best classifier for each dataset. The sensitive attributes are replaced by uniform distribution.
Adult
Bank
COMPAS
Mean
AUC (t)
%
Real
.928
.936+.000
.7994.u
88.8%
.858 002
..17
.678 008
78.8%
GReaT
.9004 008
.690+.228
.714+.010
76.8%
SMOTE
.914 600
.928..001
.772 0002
87.1%
STaSy
.883- 000
.8444.00
.722+
83.3%
TabDDPM
.906.
.977 917
.7454..00
85.6%
TabSyn
800
.918..001
.745 o01
85.8%
FairCB
.914
.907
.765
86.2%
FaiTTGAN
002
.8734.006
.703 000
Ours
.895 001 .931.002
.732 001
84.7% II
Table 9: The values of DPR and EOR with best classifier for each dataset. The sensitive attributes are replaced by uniform distribution.
Adult
Bank
COMPAS
Mean
Adult
Bank
COMPAS
Mean
DPR. (t)
%
EOR (1)
%
Real
1004
.415 oos
.790g.00
50.4%
.192 008
.368
.857 007
47.2%
CoDi
1.5.
.402 06a
99012.011
54.9%
ose
.5562.125
.923 010
60.2%
GReaT
21011.01
.622
8142.004
66.4%
.160
.4051.079
.802
45.9%
SMOTE
.31 4.017
.113+.02 026
.777=.02s
50.7%
.276 048
.359t.026
.826 041
48.
STaSy
3041.072
.532 ose
.780
53.9%
.240
.5412
.739
50.7%
TabDDPM
.811 1.04
.3414 011
.750 .03
45.7%
.191 010
.3391.000
.8164 040
44.9%
TabSyn
2864.008
.3194.018
.8154.015
47.3%
.182
.285..02
.847 0016
43.8%
FairCB
.623
.762
50.3%
2277
680+.021
18244
57. 7%
FaiTTGAN
1.11
8112..115
.6922..22 0226
66.2%
.5142.058 036
169
.7321
Ours
0017
.690+.066
.781+..00
65.8%
.580-.027
.631 +.87
.781+.015
66.4%
24


Reviews:
[{"review_id": "tvfRWa2X65", "reviewer": "Reviewer_oHWL", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths:\n- The paper reads very well. It was easy to follow the background section and the overall flow of the paper. \n- The paper studies an important problem which has an important application in many fields including the healthcare.\n- The method has shown to improve the fairness metrics in 3 datasets.\n- Comprehensive comparison against other methods. \n- Overall the paper is well-written with promising results. \n\n### Weaknesses and questions:\n- The proposed framework is claimed to be general and applicable to the case where there are many sensitive attributes, however the authors only experimented with 2 attribute at a time.\n- The method assumes that the target in the input data is already balanced. How do we expect this method work in the contexts where the data is not balanced? \n- Although the authors report the values of gamma and lambda, it is not clear how those values are selected. Are they easy to tune? \n- For the case where we have many sensitive attributes, how can we tune all these hyper-parameters for every attribute without sacrificing the quality of the generations? \n- The hyper-parameters are reported in the appendix, are they the same across all the datasets? \n- In general, since synthetic data generation is an expensive task and requires us to generate the data given a set of hyper parameters then validate the results, HP tuning is particularly expensive. How does your hyper-paramter tuning compare to other methods?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper studies the problem of mixed-type tabular synthetic data generation. The authors propose a guidance mechanism inspired by classifier free guidance to account for sensitive attributes during the generation time. The authors focus on synthesizing balanced data according to the sensitive attributes. Authors study the proposed method by comparing to several baselines and show that their approach improves fairness metrics.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- Please define µ(c,s; ws,λ) in equation 6.\n- Please make appropriate citations for the Bank Marketing and COMPAS datasets.\n- Can you provide more explanation for Figure 2 in the paper?\n- I believe we need additional explanations for the hyper-paramter tuning. \n- Optional: a new experiment that studies the effectiveness of your method in the presence of multiple sensitive attributes.", "broader_impact_concerns": "The authors provide a broader impact statement.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "SN8EQxPU4O", "reviewer": "Reviewer_VFdu", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**: \n\n1. The paper addresses the important problem of debiasing synthetic generation. \n2. The document is well-written and relatively easy to follow. \n3. The proposed method is sound and intuitive. \n4. The experiments are well conducted and demonstrate the efficiency of the proposed method in terms of fairness and fidelity to the real distribution compared to existing methods. \n\n\n\n**Weakness**:\n\n1. The related work section can be improved by thoroughly discussing other fair synthetic data generation methods beyond diffusion models\n \n2. While the proposed method is more computationally expensive (i.e., the use of attention mechanism), the gain in fairness and fidelity is not consistent across datasets and fairness metrics.  Compared to some existing methods, the benefit of the proposed method is not thoroughly provided. In particular, methods such as GReaT can be conditioned at test time to generate group-balanced datasets. Under this setup, the significance of the proposed method for fairness is unclear.  \n\n3. The method does not provide a means to control the tradeoff between fairness and accuracy. This feature can be handy to practitioners interested in improving fairness while preserving a certain level of business needs. \n\n4. It remains unclear how the method ensures balanced sampling at test time. Does the conditional sampling occur at test time to ensure a group-balanced synthetic dataset, or does every random sampling at test time yield a balanced dataset without further conditioning?\n\n5. The proposed method heavily relies on specified group labels for conditioning, making it less applicable in broader setups where demographic information is unknown due to privacy restrictions.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper proposes a method for synthesizing fair tabular data. The proposed method involves a conditioning generation process to construct group-balanced data likely to exhibit better fairness properties on downstream tasks. Experimental results show that the methods can improve the fairness of the generated data while preserving the fidelity to the original data distribution.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please see the weaknesses above.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "zAdW2tAK8w", "reviewer": "Reviewer_maQ4", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The paper extends the conditioning mechanism from labels to features, enabling fair synthetic data generation by accounting for sensitive attributes. It achieves significant improvements in fairness metrics, such as demographic parity ratio, while maintaining comparable efficacy to state-of-the-art models. This balance between fairness and performance underscores the practical value of the proposed approach. Overall, the work addresses a critical gap in fairness-aware data synthesis.\n\nThe paper lacks sufficient mathematical justification for key components, such as the multivariate extension in Equation (6), which weakens the theoretical grounding of the proposed approach. Additionally, the distinction from prior work, particularly CoDi, is not clearly articulated, making the contribution less pronounced. A minor issue is the absence of a clear definition for $\\ominus$ in the formulation of $\\mu(\\mathbf{c}, \\mathbf{s}; w_s, \\lambda)$, which could hinder reproducibility and clarity.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "1. The paper proposes a novel diffusion-based framework tailored for mixed-type tabular data synthesis, incorporating fairness as a core design principle.\n2. It introduces sensitive guidance mechanisms to balance joint distributions of sensitive attributes (e.g., sex, race) and the target label.\n3. Demonstrates superior performance in fairness metrics, such as demographic parity ratio (DPR) and equalized odds ratio (EOR), with improvements exceeding 10% compared to state-of-the-art models.\n4. Extends the capabilities of diffusion models to accommodate multiple sensitive attributes, enhancing their applicability in fairness-sensitive domains like healthcare and finance.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Clarify the Contribution Compared to CoDi \n\nWhile the paper extends conditions to features and generates fair data from a sensitive attributes perspective, the handling of categorical and continuous variables separately does not appear to be a significant novelty since this approach was previously introduced in CoDi. The authors should explicitly clarify how their approach advances beyond CoDi in terms of novelty and effectiveness. \n\nI suggest revising Section 1 and Related Work to clearly delineate the contribution, emphasizing the extension from label-only conditioning to multivariate feature-level conditioning for fairness.\n\n2. Mathematical Justification for Multivariate Guidance\n\nEquation (6) introduces multivariate guidance for conditions, extending the noise estimation to include multiple sensitive attributes. However, the mathematical justification or derivation of this formulation is unclear. Providing a theoretical foundation or citing relevant work would strengthen the validity of this approach.\n\n3. Add Mathematical Recap of Classifier-Free Guidance in Sections 2 or 3\n\nClassifier-free guidance is a fundamental aspect of the proposed method but is only briefly mentioned in the context of multivariate extensions. A concise recap of its mathematical formulation would improve accessibility and clarity, especially for readers unfamiliar with the concept.\n\nIncluding a brief mathematical explanation of classifier-free guidance in Section 2 or 3 could be beneficial. For instance, highlight how classifier-free guidance is applied and extended to multivariate conditions in Equation (6).", "broader_impact_concerns": "No immediate ethical concerns are anticipated from this research.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: While the authors have revised the manuscript to respond to the reviewers' comments, I believe that Reviewer oHWL's comments could be addressed more directly in the camera-ready version, as follows:
- More than two sensitive attributes: I think that Section 6 should also acknowledge that more sensitive attributes mean more hyperparameters as the reviewer points out, and that it may be computationally expensive to tune all of these, especially given the method's sampling time.
- Hyperparameter selection: I did not see a direct response to the reviewer's question of whether the values were the same for all datasets.
Decision: Accept (poster)