Learning interpretable models

By Stefan Rüping. 2006

107 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Model Architectures, Safety, Robustness & Interpretability, Signal Processing

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Citations in the corpus

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “re the main two reasons one might require an interpretable or explainable model. Interpretability is necessarily deﬁned in a domain-speciﬁc way [Freitas, 2014, Kodratoff, 1994, Huysmans et al., 2011, Rüping, 2006], so there cannot be an all-purpose deﬁnition. Usually, however, an interpretable machi”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “Interpretability is a domain-speciﬁc notion [Freitas, 2014, Kodratoff, 1994, Huysmans et al., 2011, Rüping, 2006], so there cannot be an all-purpose deﬁnition.”
Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models (2016, background): “Another aspect is the ability to trace back from a prediction or model component to particularly influential features in the data (Rüping, 2006) (Kim et al., 2015).”
Learning customized and optimized lists of rules with mathematical programming (2018, background): “The goal of designing interpretable classifiers has existed since the beginning of artificial intelligence, (see reviews of [21,31,52,59,68]).”
Scalable Bayesian Rule Lists (2016, neutral): “Interpretability has long since been a fundamental topic in artiﬁcial intelligence (see R¨uping, 2006; Bratko, 1997; Dawes, 1979; Vellido, Mart´ın-Guerrero, & Lisboa, 2012; Giraud-Carrier, 1998; Holte, 1993; Shmueli, 2010; Huysmans, Dejaeger, Mues, Vanthienen, & Baesens, 2011; Freitas, 2014).”
Supersparse Linear Integer Models for Predictive Scoring Systems (2013, methodology): “Existing approaches to interpretable modeling include decision trees and lists (Rüping 2006; Quinlan 1986; Rivest 1987; Letham et al. 2013).”
Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, background): “Both models have priors that can be adjusted to suit a domain-specific notion of interpretability, as it is well-known that interpretability comes in different forms for different domains [3, 15, 21, 29, 30, 39].”
Methods and Models for Interpretable Linear Classification (2014, background): “Monotonicity : Rüping [98] warns that humans tend to find a fact understandable if they are already aware of it.”
Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems (2016, methodology): “This can either proceed through regularization techniques such as Lasso [46] that attempt to pick a small subset of the most important features, or by using models that structurally match human reasoning such as Bayesian Rule Lists [47], Supersparse Linear Integer Models [48], or Probabilistic Scali”
Transformers in Medical Imaging: A Survey (2022, background): “These models have been extensively leveraged in medical domain for image segmentation [49], reconstruction [50], disease classification [51], enhancement [52], and anomaly detection [53] due to their interpretability with solid mathematical foundations and theoretical supports on the robustness, rec”
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics (2021, unknown): “It is hard to formalise it [17,21].”
SIRUS: making random forests interpretable (2019, background): “The notion of simplicity is implied whenever interpretability is invoked (e.g., Rüping, 2006; Freitas, 2014; Letham, 2015; Letham et al., 2015; Lipton, 2016; Ribeiro et al., 2016; Murdoch et al., 2019) and essentially refers to the model size, complexity, or the number of operations performed in…”
Supersparse Linear Integer Models for Interpretable Classification (2013, background): “o [51]). Accordingly, SLIM can produce scoring systems that are sparse. In statistics, sparsity refers to the number of terms in a model and constitutes the standard way of measuring model complexity [60, 63]. { Meaningful: Humans are seriously limited in estimating the association between three or ”
A Survey on the Explainability of Supervised Machine Learning (2020, background): “Criticism (local) explanation: Criticism (Rüping, 2006) supports prototypes since it detects what the prototypical instance did not capture.”