arXiv AI
FlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching
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arXiv AI
Jun 24
FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction
arXiv:2606. 24679v1 Announce Type: cross Abstract: Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning and feature transformation operators.
arXiv AI
Jun 26
Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.
arXiv Computer Vision
Oct 19
CLEAR: Cooperative Latent Flow for Visual Counterfactual Explanations with Accurate Region Preservation
The paper introduces CLEAR, a latent Flow Matching framework designed to generate visual counterfactual explanations (VCEs) that alter a classifier’s prediction while preserving realistic and decision‑irrelevant content. CLEAR addresses two main issues in latent‑space VCEs: opposing update directions between latent generation and classifier guidance, and unintended changes from VAE reconstruction. By decomposing classifier guidance into cooperative and conflicting components and selectively attenuating the conflicting part, CLEAR achieves counterfactual quality comparable to image‑space methods while being 2.5× faster.
arXiv AI
Oct 27
CounterFlow: Target-Conditioned Flow Matching for Visual Counterfactuals
arXiv:2610.35679v1 Announce Type: cross Abstract: In eXplainable AI, counterfactual explanations have gained significant traction by offering users direct, intuitive, and actionable insights into mod...
arXiv Machine Learning
Oct 15
TabFORGE: A Tabular Foundation Model for Generative Modelling
arXiv:2605.09424v2 Announce Type: replace Abstract: Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modali...
arXiv Machine Learning
Sep 18
FCx: An algorithm for finding Feasible Counterfactual Explanations
FCx is a new algorithm that generates counterfactual explanations while explicitly enforcing feasibility constraints. It uses a modified Variational Autoencoder with a multi‑factor loss to produce realistic, low‑cost counterfactuals that satisfy both hard constraints supplied by users and soft constraints inferred via causal inference. Experiments on four public datasets demonstrate that FCx matches state‑of‑the‑art performance across multiple metrics while guaranteeing feasibility.