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@inproceedings{GM19,
  author={Ghosh, Bishwamittra and Meel, Kuldeep S.},
  title={
    IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable
    Classification Rules
  },
  bib2html_dl_pdf={../Papers/aies19-gm.pdf},
  code={https://github.com/meelgroup/mlic},
  booktitle=AIES,
  month=jan,
  year={2019},
  bib2html_rescat={Formal Methods 4 ML},
  bib2html_pubtype={Refereed Conference},
  abstract={
    The wide adoption of machine learning in the critical domains such as
    medical diagnosis, law, education had propelled the need for interpretable
    techniques due to the need for end user to understand the reasoning behind
    decisions due to learning systems. The computational intractability of
    interpretable learning led practitioners to design heuristic techniques,
    which fail to provide sound handles to tradeoff accuracy and
    interpretability.
    Motivated by the success of Max-SAT solvers over the past decade, recently
    MaxSAT-based approach, called MLIC, was proposed that seeks to reduce the
    problem of learning interpretable rules expressed in Conjunctive Normal Form
    (CNF) to a MaxSAT query. While MLIC was shown to achieve accuracy similar to
    that of other state of the art black-box classifiers while generating small
    interpretable CNF formulas, the
    runtime performance of MLIC is significantly lagging and renders approach
    unusable in practice. In this context, authors raised the question: Is it
    possible to achieve the best of both worlds, i.e. a sound framework for
    interpretable learning that can take advantage of MaxSAT solvers while
    scaling to real-world instances?
    In this paper, we take a step towards answering the above question in
    affirmation. We propose an incremental approach to Max-SAT based framework
    that achieves scalable runtime performance via partition-based training
    methodology. Extensive experiments on benchmarks arising from UCI repository
    demonstrate that IMLI achieves up to three orders of magnitude runtime
    improvement without loss of accuracy and interpretability.
  },
}