Non Parametric Generative

Non-parametric generative modeling aims to create models that learn data distributions without relying on pre-defined parametric forms, offering flexibility and avoiding potential biases inherent in parametric approaches. Current research focuses on developing novel algorithms, such as those based on optimal transport, sliced Wasserstein distances, and generative flows, to improve the quality and efficiency of non-parametric generation, including conditional generation. These advancements are significant because they enable the generation of high-fidelity data while preserving privacy (through differentially private methods) and offering solutions for tasks like image generation and data imputation where parametric models may struggle. The resulting models find applications in diverse fields, including computer vision, medical imaging, and data mining.