Nicole Mücke

Stochastic Gradient Descent Meets Distribution Regression

2020 · arXiv (Cornell University)

Stochastic gradient descent (SGD) provides a simple and efficient way to solve a broad range of machine learning problems. Here, we focus on distribution regression (DR), involving two stages of sampling: Firstly, we regress from …

From inexact optimization to learning via gradient concentration

2021 · arXiv (Cornell University)

Optimization in machine learning typically deals with the minimization of empirical objectives defined by training data. However, the ultimate goal of learning is to minimize the error on future data (test error), for which the …