@inproceedings{WWB23,
  url       = "https://proceedings.neurips.cc/paper_files/paper/2023/file/46907c2ff9fafd618095161d76461842-Paper-Conference.pdf",
  author    = "Min Wu and Haoze Wu and Clark Barrett",
  title     = "VeriX: Towards Verified Explainability of Deep Neural Networks",
  booktitle = "Advances in Neural Information Processing Systems 36 (NeurIPS 2023)",
  editor    = "A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine",
  publisher = "Curran Associates, Inc.",
  pages     = "22247--22268",
  volume    = 36,
  mon       = dec,
  year      = 2023,
  category  = "Conference Publications",
  abstract  = "We present VeriX (Verified eXplainability), a system for
                  producing optimal robust explanations and generating
                  counterfactuals along decision boundaries of machine learning
                  models. We build such explanations and counterfactuals
                  iteratively using constraint solving techniques and a
                  heuristic based on feature-level sensitivity ranking. We
                  evaluate our method on image recognition benchmarks and a
                  real-world scenario of autonomous aircraft taxiing."
}

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