Atsushi Nitanda

Data Cleansing for Models Trained with SGD

2019 · arXiv (Cornell University)

Data cleansing is a typical approach used to improve the accuracy of machine learning models, which, however, requires extensive domain knowledge to identify the influential instances that affect the models. In this paper, we propose …

Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction

2023 · arXiv (Cornell University)

The mean-field Langevin dynamics (MFLD) is a nonlinear generalization of the Langevin dynamics that incorporates a distribution-dependent drift, and it naturally arises from the optimization of two-layer neural networks via (noisy) gradient descent. Recent works …