Margin-based first-order rule learning

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Exact Rule Learning via Boolean Compressed Sensing (2013, background): “Rückert & Kramer (2008) learn individual rules to maximize a quantity they define: margin minus variance, using quadratic programming.”
Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, background): “However, recent work is revisiting the problem of interpretable learning through principled optimization formulations and achieving predictive performance approaching that of uninterpretable methods [40, 24, 18, 10, 34, 30, 55, 33, 50, 20].”
Screening for learning classification rules via Boolean compressed sensing (2014, background): “As an example in a management setting, a rule-based classifier for predicting the voluntary resignation of salespeople is [7]: • Job Role = Specialty Software Sales Rep; AND • Base Salary ≤ 75,168; AND • Months Since Promoted > 13; AND • Months Since Promoted ≤ 30; AND • Quota-Based Compensation =…”
ENDER: a statistical framework for boosting decision rules (2010, background): “Rückert and Kramer (2008) gave generalization bounds based on margin minus variance objective, which is minimized in their procedure constructing a rule ensemble.”