Code
 Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models
(code)  (paper)
 FLAME  Fast Large Almost Matching Exactly
DAME  Dynamic Almost Matching Exactly FLAMEIV  Almost Matching Exactly with Instrumental Variables MALTS  Matching After Learning to Stretch
AHB  Adaptive Hyperboxes
(code)  (CRAN site)
For large scale interpretable matching in causal inference.
 Generalized and Scalable Optimal Sparse Decision Trees (GOSDT)
(code)  (benchmarking)  (paper)  (bib)
 Concept Whitening
(code)  (paper)
 Photo Upsampling via Latent Space Exploration of Generative Models (PULSE)
(project page with code and online demo)  (paper)
 Age of Unfairness
(code)  (paper)
 Interpretable Prototype Neural Networks (This Looks Like That)
(code) 
(bib)  (paper)
 Interpretable Deep Neural Networks with Hierarchical Prototypes
(code)  (paper)
 Globally Consistent SummaryExplanations
(code)  (paper)
 ROC Flexibility Data
Used for several ranking papers
(data)
 MCR  Model Class Reliance
(code)  (paper)
For assessing variable importance of a model class for a dataset.
 Optimal Sparse Decision Trees (OSDT)
(code)  (paper)
Predecessor to GOSDT (above).
 Superresolution Code from 2018 NTIRE Competition
(Webster and McCourt's code)  (Alex, Sachit, and Nikhil's code)  (paper)
 Recovery Curves
(code)
 (paper)
 Higher Dimensional Histograms
(code) 
(paper) 
(bib)
For density trees and density rule lists.
 Causal SVM
(code) 
(paper)
For estimating whether personalized treatment effects are positive, negative or neutral.
 Regulating Greed Over Time
(code) 
(paper)
 Interpretable Prototype Neural Networks from 2017 (our latest paper on this topic is better, see above)
(code) 
(bib)  (paper)
 Optimized Falling Rule Lists and Softly Falling Rule Lists
(paper)  (code)  (bib)
For classification where the probabilities decrease along the list.
 Series Finder
(code)  (paper)
For detecting crime series.
 Actionable and Interpretable Treatment Regimes (CITR)
(code) 
(paper)
For creating a policy. Uses causal inference, includes costs of gathering information, treatment, and effectiveness of the treatment.
 Certifiably Optimal RulE ListS (CORELS)
(code)  (Rbindings by Dirk Eddelbuettel)
(paper)
For classification, an alternative to decision trees. Not Bayesian, but with proof of optimality.
 Learning Optimized Risk Scores from LargeScale Datasets (RiskSLIM)
(code) 
(paper)
Creates risk assessment scoring systems.
 Interpretable Models for Recidivism Prediction
(code for processing raw data) 
(code for machine learning pipeline) 
(paper)
For reproducibility of JRSSA paper from 20152016. SLIM code is below.
 ClusteRspecific Assorted Feature selecTion (CRAFT)
(code)  (paper)
Clustering with clusterspecific feature selection.
 Scalable Bayesian Rule Lists (SBRL)
(R interface, C code  Creative Commons License)  (paper)  (bib)
For classification, an alternative to decision trees. Faster than BRL. Predecessor of CORELS (above).
 Bayesian Case Model (BCM)
(code)  (paper)
Prototype clustering with clusterspecific feature selection.
 Box Drawings for Learning with Imbalanced Data
(matlab code)  (paper)  (bib)
For imbalanced classification with realvalued features.
 Bayesian Rule Lists (BRL)
(python code  MIT license) 
(paper) 
(bib)
For classification, an alternative to decision trees. This is a predecessor of SBRL and CORELS (above).
 Bayesian Or's of And's
(code and coupon data)  (data on UCI repo)  (paper)  (bib)  (code by Ritwik Mitra, Emily Dodwell, Elena Khusainova, Deirdre Paul)
For classification, an alternative to decision trees, inductive logic programming and associative classification.
 Supersparse Linear Integer Models (SLIM)
(matlab code)  (python code)  (matlab code)  (paper)  (bib)
For building scoring systems, which are linear models with integer coefficients. Part of winning entry for 2016 INFORMS Innovative Applications in Analytics Award.
 Falling Rule Lists (FRL)
(python code)  (paper)  (bib)
For classification where the probabilities decrease along the list.
 Growing a List
(python code) 
(paper) 
(bib)
A search engine that performs set expansion. Note that this code is artificially slowed down by a restriction on the number of queries per minute, imposed by search engine companies. Unrestricted access to a search engine would eliminate this issue.
 On Combining Machine Learning with Decision Making
(code) 
(paper) 
(bib)
For new decision making framework.
