388 citations · 1.5k across the 94 of their papers we have counts for
6 papers · 1 filter
Parameter Optimization with Conscious Allocation (POCA)
Joshua Inman, Tanmay Khandait, Giulia Pedrielli +1
The performance of modern machine learning algorithms depends upon the selection of a set of hyperparameters. Common examples of hyperparameters are learning rate and the number of…
Addressing GAN Training Instabilities via Tunable Classification Losses
Monica Welfert, Gowtham R. Kurri, Kyle Otstot +1
Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting t…
A Semi-Supervised Approach for Power System Event Identification
Nima Taghipourbazargani, Lalitha Sankar, Oliver Kosut
Event identification is increasingly recognized as crucial for enhancing the reliability, security, and stability of the electric power system. With the growing deployment of Phaso…
Unifying Privacy Measures via Maximal -Leakage (MbeL)
Atefeh Gilani, Gowtham R. Kurri, Oliver Kosut +1
We introduce a family of information leakage measures called maximal -leakage (MbeL), parameterized by real numbers and greater than or equal to 1. The measure is…
Smoothly Giving up: Robustness for Simple Models
Tyler Sypherd, Nathan Stromberg, Richard Nock +2
There is a growing need for models that are interpretable and have reduced energy and computational cost (e.g., in health care analytics and federated learning). Examples of algori…
-GANs: Addressing GAN Training Instabilities via Dual Objectives
Monica Welfert, Kyle Otstot, Gowtham R. Kurri +1
In an effort to address the training instabilities of GANs, we introduce a class of dual-objective GANs with different value functions (objectives) for the generator (G) and discri…