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20182024
most citedGraph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.IT2024

Out-of-Distribution Detection using Maximum Entropy Coding

Mojtaba Abolfazli, Mohammad Zaeri Amirani, Anders Høst-Madsen +2

Given a default distribution and a set of test data this paper seeks to answer the question if it was likely that was generated by . For d…

cs.LG2022★ 1 cited

Out-of-Distribution Detection using BiGAN and MDL

Mojtaba Abolfazli, Mohammad Zaeri Arimani, Anders Host-Madsen +2

We consider the following problem: we have a large dataset of normal data available. We are now given a new, possibly quite small, set of data, and we are to decide if these are no…

cs.IT2021

Graph Compression with Application to Model Selection

Mojtaba Abolfazli, Anders Host-Madsen, June Zhang +1

Many multivariate data such as social and biological data exhibit complex dependencies that are best characterized by graphs. Unlike sequential data, graphs are, in general, unorde…

cs.LG2021★ 3 cited

Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

Mojtaba Abolfazli, Anders Host-Madsen, June Zhang +1

A classic application of description length is for model selection with the minimum description length (MDL) principle. The focus of this paper is to extend description length for…

cs.LG2019

Differential Description Length for Hyperparameter Selection in Machine Learning

Mojtaba Abolfazli, Anders Host-Madsen, June Zhang

This paper introduces a new method for model selection and more generally hyperparameter selection in machine learning. Minimum description length (MDL) is an established method fo…

cs.GT2018

How Consumer Empathy Assist Power Grid in Demand Response

Mojtaba Abolfazli, June Zhang, Anthony Kuh

This paper investigates interaction among residential electricity users and utility company in a distribution network with the capability of two-way communication provided by smart…