Learning from Multiple Outlooks

We propose a novel problem formulation of learning a single task when the data are provided in different feature spaces. Each such space is called an outlook, and is assumed to contain both labeled and unlabeled data. The objective is to take advantage of the data from all the outlooks to better classify each of the outlooks. We devise an algorithm that computes optimal affine mappings from different outlooks to a target outlook by matching moments of the empirical distributions. We further derive a probabilistic interpretation of the resulting algorithm and a sample complexity bound indicating how many samples are needed to adequately find the mapping. We report the results of extensive experiments on activity recognition tasks that show the value of the proposed approach in boosting performance.Comment: with full proofs of theorems and all experiment

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

Model and Algorithm Selection in Statistical Learning and Optimization.

Modern data-driven statistical techniques, e.g., non-linear classification and regression machine learning methods, play an increasingly important role in applied data analysis and quantitative research. For real-world we do not know a priori which methods will work best. Furthermore, most of the available models depend on so called hyper- or control parameters, which can drastically influence their performance. This leads to a vast space of potential models, which cannot be explored exhaustively. Modern optimization techniques, often either evolutionary or model-based, are employed to speed up this process. A very similar problem occurs in continuous and discrete optimization and, in general, in many other areas where problem instances are solved by algorithmic approaches: Many competing techniques exist, some of them heavily parametrized. Again, not much knowledge exists, how, given a certain application, one makes the correct choice here. These general problems are called algorithm selection and algorithm configuration. Instead of relying on tedious, manual trial-and-error, one should rather employ available computational power in a methodical fashion to obtain an appropriate algorithmic choice, while supporting this process with machine-learning techniques to discover and exploit as much of the search space structure as possible. In this cumulative dissertation I summarize nine papers that deal with the problem of model and algorithm selection in the areas of machine learning and optimization. Issues in benchmarking, resampling, efficient model tuning, feature selection and automatic algorithm selection are addressed and solved using modern techniques. I apply these methods to tasks from engineering, music data analysis and black-box optimization. The dissertation concludes by summarizing my published R packages for such tasks and specifically discusses two packages for parallelization on high performance computing clusters and parallel statistical experiments