TabChange produces more proximal and valid counterfactuals on tabular data by relationship-based flipping or adversarial latent-space attribute removal compared to baselines on seven datasets.
CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets.arXiv preprint arXiv:2009.05199,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Presents a model-agnostic framework for flow-based generative editing that operates in residual space using evolutionary self-pollination for exploitation and cross-pollination for exploration.
citing papers explorer
-
TabChange: Precise Attribute Changes in Tabular Data
TabChange produces more proximal and valid counterfactuals on tabular data by relationship-based flipping or adversarial latent-space attribute removal compared to baselines on seven datasets.