Extensive research has produced robust methods for unsupervised data visualization. Yet supervised visualization--where expert labels guide representations--remains underexplored, as most supervised approaches prioritize classification over visualization. Recently, RF-PHATE, a diffusion-based manifold learning method leveraging random forests and information geometry, marked significant progress in supervised visualization.

Counterfactual explanations play a pivotal role in explainable artificial intelligence (XAI) by offering intuitive, human-understandable alternatives that elucidate machine learning model decisions. Despite their significance, existing methods for generating counterfactuals often require constant access to the predictive model, involve computationally intensive optimization for each instance and lack the flexibility to adapt to new user-defined constraints without retraining. In this paper, we propose DiCoFlex, a novel model-agnostic, conditional generative framework that produces multiple diverse counterfactuals in a single forward pass. Leveraging conditional normalizing flows trained solely on labeled data, DiCoFlex addresses key limitations by enabling real-time user-driven customization of constraints such as sparsity and actionability at inference time. Extensive experiments on standard benchmark datasets show that DiCoFlex outperforms existing methods in terms of validity, diversity, proximity, and constraint adherence, making it a practical and scalable solution for counterfactual generation in sensitive decision-making domains.

As our first contribution, we revisit recent FSIG works and their experiments.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Onthe other hand, FSIG with extremely limited data(10samples) poses unique challenges. Inparticular, as pointed out in [4,5], severe mode collapse and loss in diversity are critical challenges in FSIG that require special attention. We remark that in [12], a technique called AdaFM is introduced to update kernels.

Task progress is intuitive and readily available task information that can guide an agent closer to the desired goal.