Relationship between classification and regression in statistical fairness
By Solenne Gaucher
Merging rate of opinions via optimal transport on random measures
By Marta Catalano
Appears in collection : 2022 - T3 - WS1 - Non-Linear and High Dimensional Inference
Although the application of deep neural networks to real-world problems has become ubiquitous, the question of why they are so effective has not yet been satisfactorily answered. However, some progress has been made in establishing an underlying mathematical foundation. This talk surveys results on statistical risk bounds of deep neural networks. In particular, we focus on the question of when neural networks bypass the curse of dimensionality. Here we discuss results for vanilla feedforward and convolutional neural networks as well as regression and classification settings.