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Found 6 papers, 2 papers with code
A Wasserstein perspective of Vanilla GANs
The assumptions of this oracle inequality are designed to be satisfied by network architectures commonly used in practice, such as feedforward ReLU networks.
AdamMCMC: Combining Metropolis Adjusted Langevin with Momentum-based Optimization
Uncertainty estimation is a key issue when considering the application of deep neural network methods in science and engineering.
Statistical guarantees for stochastic Metropolis-Hastings
A Metropolis-Hastings step is widely used for gradient-based Markov chain Monte Carlo methods in uncertainty quantification.
A PAC-Bayes oracle inequality for sparse neural networks
We study the Gibbs posterior distribution for sparse deep neural nets in a nonparametric regression setting.
Dimensionality Reduction and Wasserstein Stability for Kernel Regression
We apply the general stability result to principal component analysis (PCA).
Volatility estimation for stochastic PDEs using high-frequency observations
We study the parameter estimation for parabolic, linear, second-order, stochastic partial differential equations (SPDEs) observing a mild solution on a discrete grid in time and space.
Statistics Theory Probability Methodology Statistics Theory 62M10 (Primary), 60H15 (Secondary)
