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@inproceedings{GMM20,
  title={Classification Rules in Relaxed Logical Form},
  author={Ghosh, Bishwamittra and Malioutov, Dmitry and Meel, Kuldeep S.},
  booktitle=ECAI,
  month=jun,
  year={2020},
  bib2html_dl_pdf={../Papers/ecai20.pdf},
  code={https://github.com/meelgroup/mlic},
  bib2html_pubtype={Refereed Conference},
  bib2html_rescat={Formal Methods 4 ML},
  abstract={
    ML algorithms that produce rule-based predictions in
    Conjunctive Normal form (CNF) or in Disjunctive Normal form
    (DNF) are arguably some of the most interpretable ones. Although
    CNF/DNF rules are considered interpretable in practice, propositional
    logic has other very interpretable representations which are
    more expressive. In this paper, we generalize CNF/DNF rules and
    introduce relaxed-CNF rules, which is motivated by the popular usage
    of checklists in the medical domain. We consider relaxed definitions
    of standard OR/AND operators which allow exceptions in
    the construction of a clause and also in the selection of clauses in a
    rule. While the combinatorial structure of relaxed-CNF rules offers
    exponential succinctness, the naive learning techniques are
    computationally expensive.
    The primary contribution of this paper is to propose a novel
    incremental mini-batch learning procedure, called CRR, that employs
    advances in the combinatorial solvers and efficiently learns
    relaxed-CNF rules. Our experimental analysis demonstrates that CRR can
    generate relaxed-CNF rules which are more accurate compared to
    CNF rules and sparser compared to decision lists.
  },
}