37 citations · 123 across the 13 of their papers we have counts for
5 papers · 1 filter
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration
Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli +1
The hypothesis that sub-network initializations (lottery) exist within the initializations of over-parameterized networks, which when trained in isolation produce highly generaliza…
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri +1
With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent…
Understanding Behavior of Clinical Models under Domain Shifts
Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri
The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare. Deep learning, in particular, has been a…
Fairness GAN
Prasanna Sattigeri, Samuel C. Hoffman, Vijil Chenthamarakshan +1
In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protect…
Optimizing Kernel Machines using Deep Learning
Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri +1
Building highly non-linear and non-parametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that indu…