6 citations · 6 across the 1 of their papers we have counts for
3 papers
stat.ML2021
From inexact optimization to learning via gradient concentration
Bernhard Stankewitz, Nicole Mücke, Lorenzo Rosasco
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 e…
stat.ML2020
Stochastic Gradient Descent Meets Distribution Regression
Nicole Mücke
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…
math.ST2019★ 6 cited
Lepskii Principle in Supervised Learning
Gilles Blanchard, Peter Mathé, Nicole Mücke
In the setting of supervised learning using reproducing kernel methods, we propose a data-dependent regularization parameter selection rule that is adaptive to the unknown regulari…