A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training

Item neighbourhood methods for collaborative filtering learn a weighted graph over the set of items, where each item is connected to those it is most similar to. The prediction of a user's rating on an item is then given by that rating of neighbouring items, weighted by their similarity. This paper presents a new neighbourhood approach which we call item fields, whereby an undirected graphical model is formed over the item graph. The resulting prediction rule is a simple generalization of the classical approaches, which takes into account non-local information in the graph, allowing its best results to be obtained when using drastically fewer edges than other neighbourhood approaches. A fast approximate maximum entropy training method based on the Bethe approximation is presented, which uses a simple gradient ascent procedure. When using precomputed sufficient statistics on the Movielens datasets, our method is faster than maximum likelihood approaches by two orders of magnitude.Comment: ICML201

SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives

In this work we introduce a new optimisation method called SAGA in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates. SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates, and has support for composite objectives where a proximal operator is used on the regulariser. Unlike SDCA, SAGA supports non-strongly convex problems directly, and is adaptive to any inherent strong convexity of the problem. We give experimental results showing the effectiveness of our method.Comment: Advances In Neural Information Processing Systems, Nov 2014, Montreal, Canad

Finito: A Faster, Permutable Incremental Gradient Method for Big Data Problems

Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem. In this work we introduce a new method in this class with a theoretical convergence rate four times faster than existing methods, for sums with sufficiently many terms. This method is also amendable to a sampling without replacement scheme that in practice gives further speed-ups. We give empirical results showing state of the art performance

Remote chance of recontact

The ejection of appendages with uncertain drag characteristics presents a concern for eventual recontact. Recontact shortly after release can be prevented by avoiding ejection in a plane perpendicular to the velocity. For ejection tangential to the orbit, the likelihood of recontact within a year is high in the absence of drag and oblateness. The optimum direction of ejection of the thermal shield cable and an overestimate of the recontact probability are determined for the Cosmic Background Explorer (COBE) mission when drag, oblateness, and solar/lunar perturbations are present. The probability is small but possibly significant

Social Network for Veterans

poster abstractThe purpose of the Social Network for Veterans is to find an informal method to help Veterans stay in contact, engage in social interaction, discover areas for assistance, develop an awareness of health behavior issues concerning Veterans. The Social Network for Veterans will use animated characters (avatars) similar to characters used in Second Life or Metaverse. The Social Network will be for Veterans only. It is a collective online shared space where the Veteran can explore, meet other Veterans, socialize and participate in activities as a group or individually while chatting in real-time. Veterans are more likely to seek help from other Veterans and with a Social Network in play Veterans can offer each other help and advice no matter where their location on the planet. The Social Network will offer informative games, pet therapy, music therapy etc., social interaction, help channels, help links, health information and other basic information while providing an entertaining, safe and pleasant atmosphere

Suicide Intervention Prevention and Immersive Health Games

poster abstractMost recently, experts have recommended that interventions on social and behavioral factors related to health should link multiple levels of influence, including the individual, interpersonal, institutional, community, and policy levels (Smedley and Syme, 2000). Suicide Intervention Prevention focuses on health behavior theory of prevention through simulation. In this project, examples of causal relationships (immersion and interaction) between the characters in the simulation and the participant (player) become more meaningful and provide a unique platform to promote health education on the topic of mental health. Prevention theory enhances our work as researchers and practitioners in many ways