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Filtering by: Subject Mathematical optimization Committee Member Tadepalli, Prasad

Bayesian optimization with empirical constraints

Creator:

Azimi, Javad

Abstract:

Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...

Resource Type:

Dissertation

Full Text:

: Bayesian Optimization with Empirical Constraints Abstract approved: Xiaoli Z. Fern Bayesian Optimization
Learning greedy policies for the easy-first framework

Creator:

Xie, Jun

Abstract:

Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy policy (action scoring function) to make easy decisions first, which constrains the remaining decisions and makes them easier. This thesis studies the problem of learning...

Resource Type:

Masters Thesis

Full Text:

and principled online learn- ing algorithm that solves an optimization problem to update the weights
Machine learning in engineering : techniques to speed up numerical optimization

Creator:

Cerbone, G. (Giuseppe)

Abstract:

Many important application problems in engineering can be formalized as nonlinear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. For example, these methods depend critically on choosing a good starting point from which to perform the optimization search. In high-dimensional spaces, numerical...

Resource Type:

Dissertation

Full Text:

numerical optimization AN ABSTRACT OF THE THESIS OF Giuseppe Cerbone for the degree of Doctor of