Search Results for author:
Found 8 papers, 6 papers with code
Robust Probabilistic Time Series Forecasting
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks
The tremendous recent progress in analyzing the training dynamics of overparameterized neural networks has primarily focused on wide networks and therefore does not sufficiently address the role of depth in deep learning.
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points
Generative adversarial networks (GAN) are a widely used class of deep generative models, but their minimax training dynamics are not understood very well.
ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems and GANs
Despite remarkable empirical success, the training dynamics of generative adversarial networks (GAN), which involves solving a minimax game using stochastic gradients, is still poorly understood.
ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems
Despite remarkable empirical success, the training dynamics of generative adversarial networks (GAN), which involves solving a minimax game using stochastic gradients, is still poorly understood.
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms.
Operator Splitting Performance Estimation: Tight contraction factors and optimal parameter selection
We propose a methodology for studying the performance of common splitting methods through semidefinite programming.
Optimization and Control 47H05 47H09 68Q25 90C22 90C25 90C30 90C60
Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET
Many imaging problems, such as total variation reconstruction of X-ray computed tomography (CT) and positron-emission tomography (PET), are solved via a convex optimization problem with near-circulant, but not actually circulant, linear systems.
Optimization and Control
