5 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks
Evan Becker, Parthe Pandit, Sundeep Rangan +1
Generative Adversarial Networks (GANs) are a popular formulation to train generative models for complex high dimensional data. The standard method for training GANs involves a grad…
cs.DS2023
High Probability Bounds for Stochastic Continuous Submodular Maximization
Evan Becker, Jingdong Gao, Ted Zadouri +1
We consider maximization of stochastic monotone continuous submodular functions (CSF) with a diminishing return property. Existing algorithms only guarantee the performance \textit…
cs.LG2022★ 5 cited
Instability and Local Minima in GAN Training with Kernel Discriminators
Evan Becker, Parthe Pandit, Sundeep Rangan +1
Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data. Despite their empirical success, the training of GANs is not fully understood…