CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations

• URL: http://arxiv.org/abs/2602.17244v1
• Date: Thu, 19 Feb 2026 10:48:45 GMT
• Title: CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations
• Authors: Oleksii Furman, Patryk Marszałek, Jan Masłowski, Piotr Gaiński, Maciej Zięba, Marek Śmieja,
• Abstract summary: CounterFlowNet is a generative approach that formulates CF generation as sequential feature modification.<n>We show that CounterFlowNet achieves superior trade-offs between validity, sparsity, plausibility, and diversity with full satisfaction of the given constraints.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual explanations (CFs) provide human-interpretable insights into model's predictions by identifying minimal changes to input features that would alter the model's output. However, existing methods struggle to generate multiple high-quality explanations that (1) affect only a small portion of the features, (2) can be applied to tabular data with heterogeneous features, and (3) are consistent with the user-defined constraints. We propose CounterFlowNet, a generative approach that formulates CF generation as sequential feature modification using conditional Generative Flow Networks (GFlowNet). CounterFlowNet is trained to sample CFs proportionally to a user-specified reward function that can encode key CF desiderata: validity, sparsity, proximity and plausibility, encouraging high-quality explanations. The sequential formulation yields highly sparse edits, while a unified action space seamlessly supports continuous and categorical features. Moreover, actionability constraints, such as immutability and monotonicity of features, can be enforced at inference time via action masking, without retraining. Experiments on eight datasets under two evaluation protocols demonstrate that CounterFlowNet achieves superior trade-offs between validity, sparsity, plausibility, and diversity with full satisfaction of the given constraints.

Related papers

• Efficient Constraint-Aware Flow Matching via Randomized Exploration [10.556484074223258]
  We consider the problem of generating samples via Flow Matching (FM) with an additional requirement that the generated samples must satisfy given constraints.<n>We propose a simple adaptation of the FM objective with an additional term that penalizes the distance between the constraint set and the generated samples.<n>We numerically show that the proposed approaches achieve significant gains in terms of constraint satisfaction while matching the target distributions.
  arXiv  Detail & Related papers  (2025-08-18T19:02:02Z)
• Advancing Reliable Test-Time Adaptation of Vision-Language Models under Visual Variations [67.35596444651037]
  Vision-language models (VLMs) exhibit remarkable zero-shot capabilities but struggle with distribution shifts in downstream tasks when labeled data is unavailable.<n>We propose a Reliable Test-time Adaptation (ReTA) method that enhances reliability from two perspectives.
  arXiv  Detail & Related papers  (2025-07-13T05:37:33Z)
• Contrastive Flow Matching [61.60002028726023]
  We introduce Contrastive Flow Matching, an extension to the flow matching objective that explicitly enforces uniqueness across all conditional flows.<n>Our approach adds a contrastive objective that maximizes dissimilarities between predicted flows from arbitrary sample pairs.<n>We find that training models with Contrastive Flow Matching (1) improves training speed by a factor of up to 9x, (2) requires up to 5x fewer de-noising steps and (3) lowers FID by up to 8.9 compared to training the same models with flow matching.
  arXiv  Detail & Related papers  (2025-06-05T17:59:58Z)
• PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
  PseudoNeg-MAE enhances global feature representation of point cloud masked autoencoders by making them both discriminative and sensitive to transformations.<n>We propose a novel loss that explicitly penalizes invariant collapse, enabling the network to capture richer transformation cues while preserving discriminative representations.
  arXiv  Detail & Related papers  (2024-09-24T07:57:21Z)
• A PSO Based Method to Generate Actionable Counterfactuals for High Dimensional Data [3.0320603363468845]
  We describe an efficient and an actionable counterfactual (CF) generation method based on particle swarm optimization (PSO) An algorithm is proposed that incorporates these features and it enables greater control over the proximity and sparsity properties over the generated CFs.
  arXiv  Detail & Related papers  (2023-09-30T18:08:00Z)
• Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations [56.941276017696076]
  We propose a conceptually simple yet effective solution named Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP) CEMSP constrains changing values of abnormal features with the help of their semantically meaningful normal ranges. Compared to existing methods, we conduct comprehensive experiments on both synthetic and real-world datasets to demonstrate that our method provides more robust explanations while preserving flexibility.
  arXiv  Detail & Related papers  (2023-09-09T04:05:56Z)
• Non-Separable Multi-Dimensional Network Flows for Visual Computing [62.50191141358778]
  We propose a novel formalism for non-separable multi-dimensional network flows. Since the flow is defined on a per-dimension basis, the maximizing flow automatically chooses the best matching feature dimensions. As a proof of concept, we apply our formalism to the multi-object tracking problem and demonstrate that our approach outperforms scalar formulations on the MOT16 benchmark in terms of robustness to noise.
  arXiv  Detail & Related papers  (2023-05-15T13:21:44Z)
• CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows [11.108866104714627]
  Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. State-of-the-art counterfactual explanation methods are proposed to use variational autoencoder (VAE) to achieve promising improvements. We design a robust and efficient counterfactual explanation framework, namely CeFlow, which utilizes normalizing flows for the mixed-type of continuous and categorical features.
  arXiv  Detail & Related papers  (2023-03-26T09:51:04Z)
• GFlowOut: Dropout with Generative Flow Networks [76.59535235717631]
  Monte Carlo Dropout has been widely used as a relatively cheap way for approximate Inference. Recent works show that the dropout mask can be viewed as a latent variable, which can be inferred with variational inference. GFlowOutleverages the recently proposed probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over dropout masks.
  arXiv  Detail & Related papers  (2022-10-24T03:00:01Z)
• CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations [12.313007847721215]
  CounterNet is an end-to-end learning framework which integrates predictive model training and the generation of counterfactual (CF) explanations. Unlike post-hoc methods, CounterNet enables the optimization of the CF explanation generation only once together with the predictive model. Our experiments on multiple real-world datasets show that CounterNet generates high-quality predictions.
  arXiv  Detail & Related papers  (2021-09-15T20:09:13Z)
• Causality-based Counterfactual Explanation for Classification Models [11.108866104714627]
  We propose a prototype-based counterfactual explanation framework (ProCE) ProCE is capable of preserving the causal relationship underlying the features of the counterfactual data. In addition, we design a novel gradient-free optimization based on the multi-objective genetic algorithm that generates the counterfactual explanations.
  arXiv  Detail & Related papers  (2021-05-03T09:25:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.