TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE

In the field of Explainable AI (XAI), counterfactual (CF) explanations are one prominent method to interpret a black-box model by suggesting changes to the input that would alter a prediction. In real-world applications, the input is predominantly in tabular form and comprised of mixed data types and complex feature interdependencies. These unique data characteristics are difficult to model, and we empirically show that they lead to bias towards specific feature types when generating CFs. To overcome this issue, we introduce TABCF, a CF explanation method that leverages a transformer-based Variational Autoencoder (VAE) tailored for modeling tabular data. Our approach uses transformers to learn a continuous latent space and a novel Gumbel-Softmax detokenizer that enables precise categorical reconstruction while preserving end-to-end differentiability. Extensive quantitative evaluation on five financial datasets demonstrates that TABCF does not exhibit bias toward specific feature types, and outperforms existing methods in producing effective CFs that align with common CF desiderata.

Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study

Due to their data-driven nature, Machine Learning (ML) models are susceptible to bias inherited from data, especially in classification problems where class and group imbalances are prevalent. Class imbalance (in the classification target) and group imbalance (in protected attributes like sex or race) can undermine both ML utility and fairness. Although class and group imbalances commonly coincide in real-world tabular datasets, limited methods address this scenario. While most methods use oversampling techniques, like interpolation, to mitigate imbalances, recent advancements in synthetic tabular data generation offer promise but have not been adequately explored for this purpose. To this end, this paper conducts a comparative analysis to address class and group imbalances using state-of-the-art models for synthetic tabular data generation and various sampling strategies. Experimental results on four datasets, demonstrate the effectiveness of generative models for bias mitigation, creating opportunities for further exploration in this direction.

Procedural 3D Terrain Generation using Generative Adversarial Networks

Procedural 3D Terrain generation has become a necessity in open world games, as it can provide unlimited content, through a functionally infinite number of different areas, for players to explore. In our approach, we use Generative Adversarial Networks (GAN) to yield realistic 3D environments based on the distribution of remotely sensed images of landscapes, captured by satellites or drones. Our task consists of synthesizing a random but plausible RGB satellite image and generating a corresponding Height Map in the form of a 3D point cloud that will serve as an appropriate mesh of the landscape. For the first step, we utilize a GAN trained with satellite images that manages to learn the distribution of the dataset, creating novel satellite images. For the second part, we need a one-to-one mapping from RGB images to Digital Elevation Models (DEM). We deploy a Conditional Generative Adversarial network (CGAN), which is the state-of-the-art approach to image-to-image translation, to generate a plausible height map for every randomly generated image of the first model. Combining the generated DEM and RGB image, we are able to construct 3D scenery consisting of a plausible height distribution and colorization, in relation to the remotely sensed landscapes provided during training.