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Found 23 papers, 14 papers with code
Stabilizing Estimates of Shapley Values with Control Variates
Shapley values are among the most popular tools for explaining predictions of blackbox machine learning models.
A Generic Approach for Reproducible Model Distillation
Model distillation has been a popular method for producing interpretable machine learning.
The Infinitesimal Jackknife and Combinations of Models
In this paper we extend the Infinitesimal Jackknife to estimate the covariance between any two models.
S-LIME: Stabilized-LIME for Model Explanation
An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare.
Generalised Boosted Forests
We use these residuals and some corresponding weights to fit a base random forest and then repeat the same to obtain a boost random forest.
Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning
In 2001, Leo Breiman wrote of a divide between "data modeling" and "algorithmic modeling" cultures.
$V$-statistics and Variance Estimation
This paper develops a general framework for analyzing asymptotics of $V$-statistics.
Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models
Models which estimate main effects of individual variables alongside interaction effects have an identifiability challenge: effects can be freely moved between main effects and interaction effects without changing the model prediction.
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance
This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions.
Tree Boosted Varying Coefficient Models
This paper investigates the integration of gradient boosted decision trees and varying coefficient models.
Methodology
Unbiased Measurement of Feature Importance in Tree-Based Methods
We propose a modification that corrects for split-improvement variable importance measures in Random Forests and other tree-based methods.
Approximation Trees: Statistical Stability in Model Distillation
Here, we consider the use of regression trees as a student model, in which nodes of the tree can be used as `explanations' for particular predictions, and the whole structure of the tree can be used as a global representation of the resulting function.
Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution
This paper examines a novel gradient boosting framework for regression.
Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate
In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting.
Considerations When Learning Additive Explanations for Black-Box Models
In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations.
A Double Parametric Bootstrap Test for Topic Models
Non-negative matrix factorization (NMF) is a technique for finding latent representations of data.
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation
We compare the student model trained with distillation to a second un-distilled transparent model trained on ground-truth outcomes, and use differences between the two models to gain insight into the black-box model.
Machine Learning and the Future of Realism
Among the consequences just might be the triumph of anti-realism over realism.
Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable
Ensembles of decision trees perform well on many problems, but are not interpretable.
Bootstrap Bias Corrections for Ensemble Methods
This paper examines the use of a residual bootstrap for bias correction in machine learning regression methods.
Formal Hypothesis Tests for Additive Structure in Random Forests
While statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and the ability to conduct formal inference.
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
Instead of aggregating full bootstrap samples, we consider predicting by averaging over trees built on subsamples of the training set and demonstrate that the resulting estimator takes the form of a U-statistic.
Control Theory and Experimental Design in Diffusion Processes
We propose to accomplish this with a two-step strategy: when the full state vector of the diffusion process is observable continuously, we formulate this as an optimal control problem and apply numerical techniques from stochastic optimal control to solve it.
Methodology
