Optimizing Loss Functions: Gradient Descent Variants
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Explores coordinate descent optimization strategies, emphasizing simplicity in optimization through one-coordinate updates and discussing the implications of different approaches.
Explores the trade-off between complexity and risk in machine learning models, the benefits of overparametrization, and the implicit bias of optimization algorithms.
Explores the impact of gradient noise on optimization algorithms, focusing on smooth and nonsmooth risk functions and the derivation of gradient noise moments.
