13 citations · 41 across the 8 of their papers we have counts for
7 papers · 1 filter
Inverse classification with logistic and softmax classifiers: efficient optimization
Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada
In recent years, a certain type of problems have become of interest where one wants to query a trained classifier. Specifically, one wants to find the closest instance to a given i…
Very fast, approximate counterfactual explanations for decision forests
Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada
We consider finding a counterfactual explanation for a classification or regression forest, such as a random forest. This requires solving an optimization problem to find the close…
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions
Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev
Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years. One fundamenta…
Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features
Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán, Arman Zharmagambetov
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. Much work has foc…
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms
Miguel Á. Carreira-Perpiñán, Suryabhan Singh Hada
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.…
A flexible, extensible software framework for model compression based on the LC algorithm
Yerlan Idelbayev, Miguel Á. Carreira-Perpiñán
We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using…
An Experimental Comparison of Old and New Decision Tree Algorithms
Arman Zharmagambetov, Suryabhan Singh Hada, Miguel Á. Carreira-Perpiñán +1
This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algor…