17 citations · 21 across the 2 of their papers we have counts for
5 papers
Verifying Individual Fairness in Machine Learning Models
Philips George John, Deepak Vijaykeerthy, Diptikalyan Saha
We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or un…
Exploring the Hyperparameter Landscape of Adversarial Robustness
Evelyn Duesterwald, Anupama Murthi, Ganesh Venkataraman +2
Adversarial training shows promise as an approach for training models that are robust towards adversarial perturbation. In this paper, we explore some of the practical challenges o…
An ADMM Based Framework for AutoML Pipeline Configuration
Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy +6
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervis…
Explaining Deep Learning Models using Causal Inference
Tanmayee Narendra, Anush Sankaran, Deepak Vijaykeerthy +1
Although deep learning models have been successfully applied to a variety of tasks, due to the millions of parameters, they are becoming increasingly opaque and complex. In order t…
Debugging Machine Learning Tasks
Aleksandar Chakarov, Aditya Nori, Sriram Rajamani +2
Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of co…