388 citations · 1.5k across the 94 of their papers we have counts for
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Evaluating Multiple Guesses by an Adversary via a Tunable Loss Function
Gowtham R. Kurri, Oliver Kosut, Lalitha Sankar
We consider a problem of guessing, wherein an adversary is interested in knowing the value of the realization of a discrete random variable on observing another correlated rand…
Lower Bounds for the MMSE via Neural Network Estimation and Their Applications to Privacy
Mario Diaz, Peter Kairouz, Lalitha Sankar
The minimum mean-square error (MMSE) achievable by optimal estimation of a random variable given another random variable is of much interest i…
Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks
Andrea Pinceti, Lalitha Sankar, Oliver Kosut
A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a r…
Generation of Synthetic Multi-Resolution Time Series Load Data
Andrea Pinceti, Lalitha Sankar, Oliver Kosut
The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. We designed…
Being Properly Improper
Tyler Sypherd, Richard Nock, Lalitha Sankar
Properness for supervised losses stipulates that the loss function shapes the learning algorithm towards the true posterior of the data generating distribution. Unfortunately, data…
Realizing GANs via a Tunable Loss Function
Gowtham R. Kurri, Tyler Sypherd, Lalitha Sankar
We introduce a tunable GAN, called -GAN, parameterized by , which interpolates between various -GANs and Integral Probability Metric based GANs (under constr…