1 citations · 1 across the 5 of their papers we have counts for
5 papers
Optimal Condition for Initialization Variance in Deep Neural Networks: An SGD Dynamics Perspective
Hiroshi Horii, Sothea Has
Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a continuous-time approximation as a Fokker-P…
Gradient COBRA: A kernel-based consensual aggregation for regression
Sothea Has
In this article, we introduce a kernel-based consensual aggregation method for regression problems. We aim to exibly combine individual regression estimators usi…
Consensual Aggregation on Random Projected High-dimensional Features for Regression
Sothea Has
In this paper, we present a study of a kernel-based consensual aggregation on randomly projected high-dimensional features of predictions for regression. The aggregation scheme is…
A Kernel-based Consensual Aggregation for Regression
Sothea Has
In this article, we introduce a kernel-based consensual aggregation method for regression problems. We aim to flexibly combine individual regression estimators $r_1, r_2, \ldots, r…
A clusterwise supervised learning procedure based on aggregation of distances
Aurélie Fisher, Mathilde Mougeot, Sothea Has
Nowadays, many machine learning procedures are available on the shelve and may be used easily to calibrate predictive models on supervised data. However, when the input data consis…