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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@inproceedings{BAM25,
  title={Probabilistic Explanations for Linear Models},
  booktitle=AAAI,
  author={Subercaseaux, Bernardo and Arenas, Marcelo and Meel, Kuldeep S.},
  abstract={
    Formal XAI is an emerging field that focuses on providing explanations with
    mathematical guarantees for the decisions made by machine learning models. A
    significant amount of work in this area is centered on the computation of
    ``sufficient reasons''. Given a model $M$ and an input instance $\vec{x}$, a
    sufficient reason for the decision $M(\vec{x})$ is a subset $S$ of the
    features of $\vec{x}$ such that for any instance $\vec{z}$ that has the same
    values as $\vec{x}$ for every feature in $S$, it holds that $M(\vec{x}) =
    M(\vec{z})$. Intuitively, this means that the features in $S$ are sufficient
    to fully justify the classification of $\vec{x}$ by $M$. For sufficient
    reasons to be useful in practice, they should be as small as possible, and a
    natural way to reduce the size of sufficient reasons is to consider a
    probabilistic relaxation; the probability of $M(\vec{x}) = M(\vec{z})$ must
    be at least some value $\delta \in (0,1]$, for a random instance $\vec{z}$
    that coincides with $\vec{x}$ on the features in $S$. Computing small
    $\delta$-sufficient reasons ($\delta$-SRs) is known to be a theoretically
    hard problem; even over decision trees---traditionally deemed simple and
    interpretable models---strong inapproximability results make the efficient
    computation of small $\delta$-SRs unlikely. We propose the notion of
    $(\delta, \epsilon)$-SR, a simple relaxation of $\delta$-SRs, and show that
    this kind of explanation can be computed efficiently over linear models.
  },
  year={2025},
  month=feb,
  bib2html_rescat={Formal Methods 4 ML},
  bib2html_pubtype={Refereed Conference},
  bib2html_dl_pdf={https://arxiv.org/abs/2501.00154},
}