Leveraging Learners by Gradient

Leveraging for Regression

In this paper we examine master regression algorithms that leverage base regressors by iteratively calling them on modified samples. The most successful leveraging algorithm for classification is AdaBoost, an algorithm that requires only modest assumptions on the base learning method for its good theoretical bounds. We present three gradient descent leveraging algorithms for regression and prov...

A Geometric Approach to Leveraging

AdaBoost is a popular and eeective leveraging procedure for improving the hypotheses generated by weak learning algorithms. AdaBoost and many other leveraging algorithms can be viewed as performing a constrained gradient descent over a potential function. At each iteration the distribution over the sample given to the weak learner is the direction of steepest descent. We introduce a new leverag...

Leveraging Learners for Teaching Programming and Hardware Design at Scale

I Nefficiency of Stochastic Gradient Descent with Larger Mini - Batches ( and More Learners )

Stochastic Gradient Descent (SGD) and its variants are the most important optimization algorithms used in large scale machine learning. Mini-batch version of stochastic gradient is often used in practice for taking advantage of hardware parallelism. In this work, we analyze the effect of mini-batch size over SGD convergence for the case of general non-convex objective functions. Building on the...

Leveraging Engagement and Participation in e-Learning with Trust

This article describes a project that builds on authors previously body of knowledge on Trust and uses it to leverage higher levels of engagement in eLearning contexts. The presented research aims to investigate on unobtrusively strategies to evaluate a toolset of Trust indicators that monitor trust levels thus facilitating the deployment of trust level regulation interventions. So far results ...