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@INPROCEEDINGS{Cao:1049627,
      author       = {Cao, Zhuo and Zhao, Xuan and Krieger, Lena and Scharr,
                      Hanno and Assent, Ira},
      title        = {{L}eap{F}actual: {R}eliable {V}isual {C}ounterfactual
                      {E}xplanation {U}sing {C}onditional {F}low {M}atching},
      reportid     = {FZJ-2025-05415},
      year         = {2025},
      abstract     = {The growing integration of machine learning (ML) and
                      artificial intelligence (AI) models into high-stakes domains
                      such as healthcare and scientific research calls for models
                      that are not only accurate but also interpretable. Among the
                      existing explainable methods, counterfactual explanations
                      offer interpretability by identifying minimal changes to
                      inputs that would alter a model's prediction, thus providing
                      deeper insights. However, current counterfactual generation
                      methods suffer from critical limitations, including gradient
                      vanishing, discontinuous latent spaces, and an overreliance
                      on the alignment between learned and true decision
                      boundaries. To overcome these limitations, we propose
                      LeapFactual, a novel counterfactual explanation algorithm
                      based on conditional flow matching. LeapFactual generates
                      reliable and informative counterfactuals, even when true and
                      learned decision boundaries diverge. Following a
                      model-agnostic approach, LeapFactual is not limited to
                      models with differentiable loss functions. It can even
                      handle human-in-the-loop systems, expanding the scope of
                      counterfactual explanations to domains that require the
                      participation of human annotators, such as citizen science.
                      We provide extensive experiments on benchmark and real-world
                      datasets showing that LeapFactual generates accurate and
                      in-distribution counterfactual explanations that offer
                      actionable insights. We observe, for instance, that our
                      reliable counterfactual samples with labels aligning to
                      ground truth can be beneficially used as new training data
                      to enhance the model. The proposed method is broadly
                      applicable and enhances both scientific knowledge discovery
                      and non-expert interpretability.},
      month         = {Dec},
      date          = {2025-12-01},
      organization  = {The Thirty-Ninth Annual Conference on
                       Neural Information Processing, San
                       Diego (USA), 1 Dec 2025 - 7 Dec 2025},
      subtyp        = {Invited},
      cin          = {IAS-8},
      cid          = {I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2025-05415},
      url          = {https://juser.fz-juelich.de/record/1049627},
}