Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

Diversity in action : minority group media and social change

My thesis consists of a creative work plus an exegesis. This exegesis uses case study research to investigate three Brisbane-based media organisations and the role they play in encouraging social inclusion and other positive social change for specific disadvantaged and stigmatised minority groups. Bailey, Cammaerts and Carpentier’s theoretical approach to alternative media forms the basis of this research. Bailey et al. (2008, p. 156) view alternative media organisations as having four important roles, two media-centred and two society-centred, which must all be considered to best understand them: • serving their communities • acting as an alternative to mainstream media discourses • promoting and advocating democratisation in the media and through the media in society • functioning as a crossroads in civil society. The first case study, about community radio station 4RPH (Radio for the Print Handicapped), centres on promoting social inclusion for people with a print disability through access to printed materials (primarily mainstream print media) in an audio format. The station also provides important opportunities for members of this group to produce media and, to a lesser extent, provides disability-specific information and discussions. The second case study, about gay print and online magazine Queensland Pride, focuses on promoting social inclusion and combating the discrimination and repression of people who identify as lesbian, gay, bisexual or transgender. Central issues include the representation (including sexualised representation) of a subculture and niche target market, and the impact of commercialisation on this free publication. The third case study, about community radio station 98.9FM, explores the promotion of social inclusion for peoples whose identity, cultures, issues, politics and contributions are often absent or misrepresented in the mainstream media. This radio station provides “a first level of service” (Meadows & van Vuuren, 1998, p. 104) to these people, but also informs and entertains those in the majority society. The findings of this research suggest that there are two key mechanisms that help these media organisations to effect social change: first, strengthening the minority community and serving its needs, and second, fostering connections with the broader society.

Online banking customization via tag-based interaction

In this paper, we describe ongoing work on online banking customization with a particular focus on interaction. The scope of the study is confined to the Australian banking context where the lack of customization is evident. This paper puts forward the notion of using tags to facilitate personalized interactions in online banking. We argue that tags can afford simple and intuitive interactions unique to every individual in both online and mobile environments. Firstly, through a review of related literature, we frame our work in the customization domain. Secondly, we define a range of taggable resources in online banking. Thirdly, we describe our preliminary prototype implementation with respect to interaction customization types. Lastly, we conclude with a discussion on future work.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

High-probability regret bounds for bandit online linear optimization

We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ∗ ( √ T) against an adaptive adversary. This improves on the previous algorithm [8] whose regret is bounded in expectation against an oblivious adversary. We obtain the same dependence on the dimension (n 3/2) as that exhibited by Dani et al. The results of this paper rest firmly on those of [8] and the remarkable technique of Auer et al. [2] for obtaining high probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.