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Filtering by: Creator Stockett, Alan Lee Degree Field Computer Science Subject Mathematical optimization

Bayesian methods for knowledge transfer and policy search in reinforcement learning

Creator:

Wilson, Aaron

Abstract:

How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...

Resource Type:

Dissertation

Full Text:

Tadepalli Alan Fern How can an agent generalize its knowledge to new circumstances? To learn effectively
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:

dissertation has been done with the collaboration of Alan Fern which was a great experience for me. I would
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:

system. Lee et al. [24] and Stoyanov and Eisner [39] subsequently applied the same idea to corefer