A SAT-Based Approach to Learn Explainable Decision Sets

By Alexey Ignatiev, Filipe Pereira, Nina Narodytska, Joao Marques-Silva. 2018

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “…Ertekin, 2016; Yu et al., 2020a; Angelino et al., 2017, 2018; Aı̈vodji et al., 2019) and decision sets (Wang and Rudin, 2015b; Lakkaraju et al., 2016; Ignatiev et al., 2018; Dash et al., 2018b; Malioutov and Meel, 2018; Ghosh and Meel, 2019; Dhamnani et al., 2019; Yu et al., 2020b; Cao et al.,…”
Optimal Decision Lists using SAT (2020, methodology): “We compare our prototype against state of the art in perfect decision set methods (Ignatiev et al. 2018; Yu et al. 2020), namely mds 2 , mds 2 , and opt .”
Learning Explainable Decision Rules via Maximum Satisfiability (2020, methodology): “Following advancements in SAT-solving, the problem has also been cast into various maximum-satisfiability (MAX-SAT)-based frameworks [12]–[14].”
Towards Trustable Explainable AI (2020, background): “…et al. , 2018b; Katz et al. , 2017] but also motivated efforts aiming at devising approaches to ex-plainable artiﬁcial intelligence (XAI) [Ribeiro et al. , 2018; Lundberg and Lee, 2017; Ignatiev et al. , 2018; Narodytska et al. , 2018a; Ribeiro et al. , 2016; Ignatiev et al. , 2019a; Monroe, 2018].”
On Relating Explanations and Adversarial Examples (2019, methodology): “When considering interpretable ML models, the goal is to compute models that provide minimal explanations associated with each prediction [28, 2, 39, 24, 49]2.”
Abduction-Based Explanations for Machine Learning Models (2018, methodology): “…2017), but also efforts to explain predictions made by such models (Evans and Grefenstette 2018; Li et al. 2018; Ribeiro, Singh, and Guestrin 2018; Ignatiev et al. 2018; Montavon, Samek, and M ¨ uller 2018; Ribeiro, Singh, and Guestrin 2016; Lakkaraju, Bach, and Leskovec 2016; Baehrens et al.…”
SAT-Based Encodings for Optimal Decision Trees with Explicit Paths (2020, background): “Synthesis by calls to a SAT/SMT solver has seen increased interest in the recent years, cf. [19,21,28].”
On Explaining Decision Trees (2020, background): “Motivated by this perception, there has been extensive work on learning DTs (and related logic ML models) with properties deemed important for interpretability, e.g. number of nodes, maximum/average depth, etc. [2,68,25,32,1,7,57,24,26,70,67,58,44,30,17,5,73,35,4,78,69,10,34,72,71,36,46,11,45] 2 .”