High Dimensional Noise

High-dimensional noise presents a significant challenge in data analysis, hindering accurate inference and model training across diverse fields. Current research focuses on developing robust algorithms and model architectures, such as normalizing flows and kernel-based methods, to mitigate the effects of this noise, particularly in scenarios involving manifold learning and generative modeling (e.g., GANs). These efforts aim to improve the accuracy and reliability of data analysis in high-dimensional settings, with applications ranging from semiconductor manufacturing process control to single-cell RNA sequencing analysis. The development of effective noise-robust techniques is crucial for unlocking the potential of increasingly complex datasets.