activity
20172021
most citedOn the Trustworthiness of Tree Ensemble Explainability Methods

15 citations · 23 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG202115 cited

On the Trustworthiness of Tree Ensemble Explainability Methods

Angeline Yasodhara, Azin Asgarian, Diego Huang +1

The recent increase in the deployment of machine learning models in critical domains such as healthcare, criminal justice, and finance has highlighted the need for trustworthy meth…

cs.CY2019

Prediction of Workplace Injuries

Mehdi Sadeqi, Azin Asgarian, Ariel Sibilia

Workplace injuries result in substantial human and financial losses. As reported by the International Labour Organization (ILO), there are more than 374 million work-related injuri…

cs.CV20198 cited

Limitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia

Azin Asgarian, Shun Zhao, Ahmed B. Ashraf +5

Accurate facial expression analysis is an essential step in various clinical applications that involve physical and mental health assessments of older adults (e.g. diagnosis of pai…

cs.LG2018

A Hybrid Instance-based Transfer Learning Method

Azin Asgarian, Parinaz Sobhani, Ji Chao Zhang +4

In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models…

cs.CV2017

Subspace Selection to Suppress Confounding Source Domain Information in AAM Transfer Learning

Azin Asgarian, Ahmed Bilal Ashraf, David Fleet +1

Active appearance models (AAMs) are a class of generative models that have seen tremendous success in face analysis. However, model learning depends on the availability of detailed…