提出新方法让模型推荐更可靠，即使模型有变化也能保持稳定。
ElliCE: Efficient and Provably Robust Algorithmic Recourse via the Rashomon Sets
- 用椭球体近似所有近优模型，统一优化推荐方案。
- 在真实数据集上，推荐结果比基线更鲁棒且速度快3倍以上。
- 适合关注模型不确定性、需要可解释推荐的场景。
机器学习模型影响人们的生活决策，理解其预测结果之外，还需知道个体如何行动才能获得更好结果。算法型补救提供可操作的输入修改建议以获得更优结果，通常依赖反事实解释来提出调整方案。然而，当近优模型集合（即Rashomon集）较大时，标准反事实解释可能不可靠——某一模型有效的建议在另一模型下可能失效。我们提出ElliCE框架，通过椭球体近似Rashomon集，对反事实进行优化。生成的解释在该椭球体内具有理论保证的唯一性、稳定性及与关键特征方向的一致性。实验表明，ElliCE生成的反事实不仅更鲁棒，且更具灵活性，能适应用户指定的特征约束，同时比现有基线快得多。这为模型不确定性下的可靠补救提供了原则性且实用的解决方案，确保模型演化过程中推荐结果依然稳定。
原文摘要 · Abstract (English)
Machine learning models now influence decisions that directly affect people's lives, making it important to understand not only their predictions, but also how individuals could act to obtain better results. Algorithmic recourse provides actionable input modifications to achieve more favorable outcomes, typically relying on counterfactual explanations to suggest such changes. However, when the Rashomon set - the set of near-optimal models - is large, standard counterfactual explanations can become unreliable, as a recourse action valid for one model may fail under another. We introduce ElliCE, a novel framework for robust algorithmic recourse that optimizes counterfactuals over an ellipsoidal approximation of the Rashomon set. The resulting explanations are provably valid over this ellipsoid, with theoretical guarantees on uniqueness, stability, and alignment with key feature directions. Empirically, ElliCE generates counterfactuals that are not only more robust but also more flexible, adapting to user-specified feature constraints while being substantially faster than existing baselines. This provides a principled and practical solution for reliable recourse under model uncertainty, ensuring stable recommendations for users even as models evolve.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。