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09/16/2023
Inverse classification with logistic and softmax classifiers: efficient optimization
In recent years, a certain type of problems have become of interest wher...
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08/30/2023
Solving Recurrence Relations using Machine Learning, with Application to Cost Analysis
Automatic static cost analysis infers information about the resources us...
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03/06/2023
Very fast, approximate counterfactual explanations for decision forests
We consider finding a counterfactual explanation for a classification or...
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07/09/2021
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions
Model compression is generally performed by using quantization, low-rank...
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04/07/2021
Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features
The widespread deployment of deep nets in practical applications has lea...
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03/01/2021
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms
We consider counterfactual explanations, the problem of minimally adjust...
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05/15/2020
A flexible, extensible software framework for model compression based on the LC algorithm
We propose a software framework based on the ideas of the Learning-Compr...
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11/25/2019
Structured Multi-Hashing for Model Compression
Despite the success of deep neural networks (DNNs), state-of-the-art mod...
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11/08/2019
An Experimental Comparison of Old and New Decision Tree Algorithms
This paper presents a detailed comparison of a recently proposed algorit...
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09/27/2019
Style Transfer by Rigid Alignment in Neural Net Feature Space
Arbitrary style transfer is an important problem in computer vision that...
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07/13/2017
Model compression as constrained optimization, with application to neural nets. Part II: quantization
We consider the problem of deep neural net compression by quantization: ...
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07/05/2017
Model compression as constrained optimization, with application to neural nets. Part I: general framework
Compressing neural nets is an active research problem, given the large s...
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05/30/2016
ParMAC: distributed optimisation of nested functions, with application to learning binary autoencoders
Many powerful machine learning models are based on the composition of mu...
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02/04/2016
An ensemble diversity approach to supervised binary hashing
Binary hashing is a well-known approach for fast approximate nearest-nei...
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03/02/2015
A review of mean-shift algorithms for clustering
A natural way to characterize the cluster structure of a dataset is by f...
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01/21/2015
Optimizing affinity-based binary hashing using auxiliary coordinates
In supervised binary hashing, one wants to learn a function that maps a ...
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01/05/2015
Hashing with binary autoencoders
An attractive approach for fast search in image databases is binary hash...
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12/29/2014
An ADMM algorithm for solving a proximal bound-constrained quadratic program
We consider a proximal operator given by a quadratic function subject to...
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05/26/2014
The role of dimensionality reduction in linear classification
Dimensionality reduction (DR) is often used as a preprocessing step in c...
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05/23/2014
LASS: a simple assignment model with Laplacian smoothing
We consider the problem of learning soft assignments of N items to K cat...
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09/06/2013
Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application
We provide an elementary proof of a simple, efficient algorithm for comp...
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04/24/2013
The K-modes algorithm for clustering
Many clustering algorithms exist that estimate a cluster centroid, such ...
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12/24/2012
Distributed optimization of deeply nested systems
In science and engineering, intelligent processing of complex signals su...
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09/15/2011
Reconstruction of sequential data with density models
We introduce the problem of reconstructing a sequence of multidimensiona...
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08/14/2011