research
∙
05/23/2023
Layer-wise Adaptive Step-Sizes for Stochastic First-Order Methods for Deep Learning
We propose a new per-layer adaptive step-size procedure for stochastic f...
share
research
∙
02/08/2022
A Mini-Block Natural Gradient Method for Deep Neural Networks
The training of deep neural networks (DNNs) is currently predominantly d...
research
∙
03/09/2021
Optimal Pricing with a Single Point
We study the following fundamental data-driven pricing problem. How can/...
research
∙
06/16/2020
Practical Quasi-Newton Methods for Training Deep Neural Networks
We consider the development of practical stochastic quasi-Newton, and in...
research
∙
03/30/2020
Stochastic Flows and Geometric Optimization on the Orthogonal Group
We present a new class of stochastic, geometrically-driven optimization ...
research
∙
12/31/2019
A Dynamic Sampling Adaptive-SGD Method for Machine Learning
We propose a stochastic optimization method for minimizing loss function...
research
∙
02/11/2019