Many-Objectives Optimization: A Machine Learning Approach for Reducing the Number of Objectives

Fast optimization of non-convex Machine Learning objectives

In this project we examined the problem of non-convex optimization in the context of Machine Learning, drawing inspiration from the increasing popularity of methods such as Deep Belief Networks, which involve non-convex objectives. We focused on the task of training the Neural Autoregressive Distribution Estimator, a recently proposed variant of the Restricted Boltzmann Machine, in applications...

Efficient optimization of many objectives by approximation-guided evolution

Multi-objective optimization problems arise frequently in applications, but can often only be solved approximately by heuristic approaches. Evolutionary algorithms have been widely used to tackle multi-objective problems. These algorithms use different measures to ensure diversity in the objective space but are not guided by a formal notion of approximation. We present a framework for evolution...

On the evolutionary optimisation of many objectives

Conditions for Convergence in Regularized Machine Learning Objectives

Analysis of the convergence rates of modern convex optimization algorithms can be achived through binary means: analysis of emperical convergence, or analysis of theoretical convergence. These two pathways of capturing information diverge in efficacy when moving to the world of distributed computing, due to the introduction of non-intuitive, non-linear slowdowns associated with broadcasting, an...

Design and evaluation of a national set of learning objectives: the medical physiology learning objectives project.

In 1998, the American Physiological Society (APS) and the Association of Chairs of Departments of Physiology (ACDP) began collaboration on a project to develop a set of physiology learning objectives for medical students. Over the next 2 years, more than 50 physiologists collaborated in the development a comprehensive draft containing its 695 learning objectives. Faculty in 31 medical schools i...