In many applications, training data is provided in the form of related datasets obtained from several sources, which typically affects the sample distribution. The learned classification models, which are expected to perform well on similar data coming from new sources, often suffer due to bias introduced by what we call `spurious' samples -- those due to source characteristics and not representative of any other part of the data. As standard outlier detection and robust classification usually fall short of determining groups of spurious samples, we propose a procedure which identifies the common structure across datasets by minimizing a multi-dataset divergence metric, increasing accuracy for new datasets.

In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate rankings exist in the literature, here we focus on the Area Under the ROC Curve (AUC) as it widely used and has a strong theoretical underpinning. In practical recommendation, only items at the top of the ranked list are presented to the users. With this in mind we propose a class of objective functions which primarily represent a smooth surrogate for the real AUC, and in a special case we show how to prioritise the top of the list. This loss is differentiable and is optimised through a carefully designed stochastic gradient-descent-based algorithm which scales linearly with the size of the data. We mitigate sample bias present in the data by sampling observations according to a certain power-law based distribution. In addition, we provide computation results as to the efficacy of the proposed method using synthetic and real data.

As the premier international forumon human-computer interaction, "ACM Conference on Human Factors in ComputingSystems" (CHI), has continued to grow and broaden its range of topics and contributing disciplines. CHI 2014 received over 2000 submissions. Those papers and notes were from diversified research domains — psychologists and computer scientists began to meet new visions from sociology, engineering and manufacturing, communication sciences, design and arts, among others. Here, I would like to introduce progress in HCI research which will bring new opportunities and challenges to AI community.

Understanding the nature of strategic voting is the holy grail of social choice theory, where game-theory, social science and recently computational approaches are all applied in order to model the incentives and behavior of voters. In a recent paper, Meir et al.[EC'14] made another step in this direction, by suggesting a behavioral game-theoretic model for voters under uncertainty. For a specific variation of best-response heuristics, they proved initial existence and convergence results in the Plurality voting system. This paper extends the model in multiple directions, considering voters with different uncertainty levels, simultaneous strategic decisions, and a more permissive notion of best-response. It is proved that a voting equilibrium exists even in the most general case. Further, any society voting in an iterative setting is guaranteed to converge to an equilibrium. An alternative behavior is analyzed, where voters try to minimize their worst-case regret. As it turns out, the two behaviors coincide in the simple setting of Meir et al.[EC'14], but not in the general case.

Narrative generation represents an application domain for AI planning where plan quality is related to properties such as shape of plan trajectory. In our work we have developed a plan-based approach to narrative generation that uses character relationships as a key determinant in controlling plan shape (relationships are key in genres such as serial dramas and soaps). Our approach is implemented in a demonstration Interactive Narrative, called NetworkING, set in the medical drama genre. The system features a user-friendly mechanism for specifying relationships between virtual characters, via a social network and real-time visualisation of generated narratives on a 3D stage.

Two other algorithms require the knowledge Markov Decision Processes (MDPs) offer a general framework of the optimal policy and its expected reward. We show to describe probabilistic planning problems of varying that the expected reward of the optimal policy is a lower complexity. The development of algorithms that act successfully bound for the expected performance of both strategies. in MDPs is important to many AI applications. Our final algorithm switches between the application of Since it is often impossible or intractable to evaluate MDP the optimal policy and the policy with the highest possible algorithms based on a theoretical analysis alone, the International outcome, which can be computed without notable overhead Probabilistic Planning Competition (IPPC) was introduced in the Trial-based Heuristic Tree Search (THTS) framework to allow a comparison based on experimental evaluation. (Keller and Helmert 2013). We show theoretically and empirically The idea is to approximate the quality of an MDP that all algorithms outperform the naïve base approach solver by performing a sequence of runs on a problem instance, that ignores the potential of optimizing evaluation and by using the average of the obtained results as runs in hindsight, and that it pays off to take suboptimal base an approximation of the expected reward.

As the proliferation of constant data feeds increases from social media, embedded sensors, and other sources, the capability to provide predictive concept labels to these data streams will become ever more important and lucrative. However, the dynamic, non-stationary nature, and effectively infinite length of data streams pose additional challenges for stream data mining algorithms. The sparse quantity of training data also limits the use of algorithms that are heavily dependent on supervised training. To address all these issues, we propose an incremental semi-supervised method that provides general concept class label predictions, but it also tracks concept clusters within the feature space using an innovative new online clustering algorithm. Each concept cluster contains an embedded stream classifier, creating a diverse ensemble for data instance classification within the generative model used for detecting emerging concepts in the stream. Unlike other recent novel class detection methods, our method goes beyond detecting, and continues to differentiate and track the emerging concepts. We show the effectiveness of our method on several synthetic and real world data sets, and we compare the results against other leading baseline methods.

Tag recommendation has become one of the most important ways of organizing and indexing online resources like articles, movies, and music. Since tagging information is usually very sparse, effective learning of the content representation for these resources is crucial to accurate tag recommendation. Recently, models proposed for tag recommendation, such as collaborative topic regression and its variants, have demonstrated promising accuracy. However, a limitation of these models is that, by using topic models like latent Dirichlet allocation as the key component, the learned representation may not be compact and effective enough. Moreover, since relational data exist as an auxiliary data source in many applications, it is desirable to incorporate such data into tag recommendation models. In this paper, we start with a deep learning model called stacked denoising autoencoder (SDAE) in an attempt to learn more effective content representation. We propose a probabilistic formulation for SDAE and then extend it to a relational SDAE (RSDAE) model. RSDAE jointly performs deep representation learning and relational learning in a principled way under a probabilistic framework. Experiments conducted on three real datasets show that both learning more effective representation and learning from relational data are beneficial steps to take to advance the state of the art.

We develop the Dynamic Chinese Restaurant Process (DCRP) which incorporates time-evolutionary feature in dependent Dirichlet Process mixture models. This model can capture the dynamic change of mixture components, allowing clusters to emerge, vanish and vary over time. All these macroscopic changes are controlled by tracing the birth and death of every single element. We investigate the  properties of dependent Dirichlet Process mixture model based on DCRP and develop corresponding Gibbs Sampler for posterior inference. We also conduct simulation and empirical studies to compare this model with traditional CRP and related models. The results show that this model can provide better results for sequential data, especially for data with heterogeneous lifetime distribution.

Planning for and controlling a network of interacting devices requires a planner that accounts for the automatic timed transitions of devices, while meeting deadlines and achieving durative goals. Consider a planner for an imaging satellite with a camera that cannot tolerate exhaust. The planner would need to determine that opening a valve causes a chain reaction that ignites the engine, and thus needs to shield the camera. While planners exist that support deadlines and durative goals, currently, no planners can handle automatic timed transitions. We present tBurton, a temporal planner that supports these features, while additionally producing a temporally least-commitment plan. tBurton uses a divide and conquer approach: dividing the problem using causal-graph decomposition and conquering each factor with heuristic forward search. The `sub-plans' from each factor are then unified in a conflict directed search, guided by the causal graph structure. We describe why this approach is fast and efficient, and demonstrate its ability to improve the performance of existing planners on factorable problems through benchmarks from the International Planning Competition.