13 citations · 41 across the 8 of their papers we have counts for
8 papers
A Machine Learning-based Approach for Solving Recurrence Relations and its use in Cost Analysis of Logic Programs
Louis Rustenholz, Maximiliano Klemen, Miguel Ángel Carreira-Perpiñán +1
Automatic static cost analysis infers information about the resources used by programs without actually running them with concrete data, and presents such information as functions…
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…