The supervised hierarchical Dirichlet process

We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method on two real-world classification problems and two real-world regression problems. Bayesian nonparametric regression models based on the Dirichlet process, such as the Dirichlet process-generalised linear models (DP-GLM) have previously been explored; these models allow flexibility in modelling nonlinear relationships. However, until now, Hierarchical Dirichlet Process (HDP) mixtures have not seen significant use in supervised problems with grouped data since a straightforward application of the HDP on the grouped data results in learnt clusters that are not predictive of the responses. The sHDP solves this problem by allowing for clusters to be learnt jointly from the group structure and from the label assigned to each group.Comment: 14 page

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

From Natural Language Specifications to Program Input Parsers

We present a method for automatically generating input parsers from English specifications of input file formats. We use a Bayesian generative model to capture relevant natural language phenomena and translate the English specification into a specification tree, which is then translated into a C++ input parser. We model the problem as a joint dependency parsing and semantic role labeling task. Our method is based on two sources of information: (1) the correlation between the text and the specification tree and (2) noisy supervision as determined by the success of the generated C++ parser in reading input examples. Our results show that our approach achieves 80.0\% F-Score accuracy compared to an F-Score of 66.7\% produced by a state-of-the-art semantic parser on a dataset of input format specifications from the ACM International Collegiate Programming Contest (which were written in English for humans with no intention of providing support for automated processing)National Science Foundation (U.S.) (Grant IIS-0835652)Battelle Memorial Institute (PO #300662

An Exploratory Case Study of a Quality Assurance Process at an Ontario University

Currently, quality assurance is a widespread global practice in higher education. This exploratory case study at one Ontario university uses a Foucauldian-informed post-structuralist discourse analysis to interrogate the definition of ‘quality’ as it relates to quality assurance. More specifically, this study hopes to raise an awareness that what constitutes quality is taken for granted in quality assurance practices for universities. From an examination of resource documents and interviews with faculty administrators (n=12), the key findings of this study expose an over-arching neoliberal discursive framing of quality and quality assurance. The marketization of higher education leads to an over-emphasis on procedural compliance and a propensity to quantify educational experiences. There was an incongruence between the current approach to quality assurance and education. This research argues that an economic lens borrowed from the business sector is problematic because it assumes principles used in manufacturing a material product can be applied to something as intangible and transformative as education. This research calls on educational leaders to critically reflect on underlying assumptions to expose the power struggles embedded in our quality assurance discourses in order to better understand how dominant ideology obscures other perspectives. An understanding of how quality can be defined from different perspectives disrupts the assumption that a neoliberal economic lens is the only way to view academic quality. Those engaged in administering quality assurance are urged to challenge dominant neoliberal premises and consider alternatives that apply an ethical lens to higher education