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Found 7 papers, 1 papers with code
Inverse classification with logistic and softmax classifiers: efficient optimization
Specifically, one wants to find the closest instance to a given input instance such that the classifier's predicted label is changed in a desired way.
Very fast, approximate counterfactual explanations for decision forests
We consider finding a counterfactual explanation for a classification or regression forest, such as a random forest.
Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features
The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction.
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms
We consider counterfactual explanations, the problem of minimally adjusting features in a source input instance so that it is classified as a target class under a given classifier.
An Experimental Comparison of Old and New Decision Tree Algorithms
This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algorithms.
Style Transfer by Rigid Alignment in Neural Net Feature Space
Arbitrary style transfer is an important problem in computer vision that aims to transfer style patterns from an arbitrary style image to a given content image.
Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets
This inverse set is a complicated high dimensional object that we explore by an optimization-based sampling approach.
