20 citations · 40 across the 9 of their papers we have counts for
13 papers
Loss Balancing for Fair Supervised Learning
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan
Supervised learning models have been used in various domains such as lending, college admission, face recognition, natural language processing, etc. However, they may inherit pre-e…
Revisiting DeepFool: generalization and improvement
Alireza Abdollahpoorrostam, Mahed Abroshan, Seyed-Mohsen Moosavi-Dezfooli
Deep neural networks have been known to be vulnerable to adversarial examples, which are inputs that are modified slightly to fool the network into making incorrect predictions. Th…
Safe AI for health and beyond -- Monitoring to transform a health service
Mahed Abroshan, Michael Burkhart, Oscar Giles +7
Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems oft…
Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions
Mahed Abroshan, Saumitra Mishra, Mohammad Mahdi Khalili
One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a…
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili +2
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. However, this assumption is not satisfied in man…
Fair Sequential Selection Using Supervised Learning Models
Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan
We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. At each time step, a decision maker accepts or rejects the given…