Zeyu Wang

We design a simple but surprisingly effective visual recognition benchmark for studying bias mitigation, propose a domain-independent training technique that outperforms all other methods. Expand

We examine the robustness of scagnostics through a series of theoretical and empirical studies. Expand

We develop analytical confidence intervals for adaptive Kriging-based reliability analysis for complex limit states by leveraging statistical properties of Poisson Binomial distribution for the expected number of failure points and Lindeberg's condition for central limit theorem. Expand

We address these limitations by proposing a new approach to reliability updating called RUAK, which uses the efficient Kriging meta-model, which significantly enhances the computational efficiency. Expand