
Evaluation Criteria for Instancebased Explanation
Explaining predictions made by complex machine learning models helps use...
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Interpretable Companions for BlackBox Models
We present an interpretable companion model for any pretrained blackbo...
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Data Cleansing for Models Trained with SGD
Data cleansing is a typical approach used to improve the accuracy of mac...
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Enumeration of Distinct Support Vectors for Interactive Decision Making
In conventional prediction tasks, a machine learning algorithm outputs a...
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Fairwashing: the risk of rationalization
Blackbox explanation is the problem of explaining how a machine learnin...
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Pretending Fair Decisions via Stealthily Biased Sampling
Fairness by decisionmakers is believed to be auditable by third parties...
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Quantile regression approach to conditional mode estimation
In this paper, we consider estimation of the conditional mode of an outc...
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Convex Hull Approximation of Nearly Optimal Lasso Solutions
In an ordinary feature selection procedure, a set of important features ...
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Maximizing Invariant Data Perturbation with Stochastic Optimization
Feature attribution methods, or saliency maps, are one of the most popul...
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Maximally Invariant Data Perturbation as Explanation
While several feature scoring methods are proposed to explain the output...
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On Estimation of Conditional Modes Using Multiple Quantile Regressions
We propose an estimation method for the conditional mode when the condit...
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Consistent Nonparametric DifferentFeature Selection via the Sparsest kSubgraph Problem
Twosample feature selection is the problem of finding features that des...
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Finding Alternate Features in Lasso
We propose a method for finding alternate features missing in the Lasso ...
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Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach
Tree ensembles, such as random forests and boosted trees, are renowned f...
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Making Tree Ensembles Interpretable
Tree ensembles, such as random forest and boosted trees, are renowned fo...
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Anomaly detection in reconstructed quantum states using a machinelearning technique
The accurate detection of small deviations in given density matrices is ...
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Learning a Common Substructure of Multiple Graphical Gaussian Models
Properties of data are frequently seen to vary depending on the sampled ...
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Satoshi Hara
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