We consider an online learning problem (classification or prediction) involving disparate sources of sequentially arriving data, whereby a user over time learns the best set of data sources to use in constructing the classifier by exploiting their similarity. We first show that, when (1) the similarity information among data sources is known, and (2) data from different sources can be acquired without cost, then a judicious selection of data from different sources can effectively enlarge the training sample size compared to using a single data source, thereby improving the rate and performance of learning; this is achieved by bounding the classification error of the resulting classifier. We then relax assumption (1) and characterize the loss in learning performance when the similarity information must also be acquired through repeated sampling. We further relax both (1) and (2) and present a cost-efficient algorithm that identifies a best crowd from a potentially large set of data sources in terms of both classifier performance and data acquisition cost. This problem has various applications, including online prediction systems with time series data of various forms, such as financial markets, advertisement and network measurement.

We study the problem of computing Nash equilibria of zero-sum games.Many natural zero-sum games have exponentially many strategies, but highly structured payoffs. For example, in the well-studied Colonel Blotto game (introduced by Borel in 1921), players must divide a pool of troops among a set of battlefields with the goal of winning (i.e., having more troops in) a majority. The Colonel Blotto game is commonly used for analyzing a wide range of applications from the U.S presidential election, to innovative technology competitions, toadvertisement, to sports.However, because of the size of the strategy space, standard methods for computing equilibria of zero-sum games fail to be computationally feasible.Indeed, despite its importance, only few solutions for special variants of the problem are known. In this paper we show how to compute equilibria of Colonel Blotto games. Moreover, our approach takes the form of a general reduction: to find a Nash equilibrium of a zero-sum game, it suffices to design a separation oracle for the strategy polytope of any bilinear game that is payoff-equivalent. We then apply this technique to obtain the first polytime algorithms for a variety of games. In addition to Colonel Blotto, we also show how to compute equilibria in an infinite-strategy variant called the General Lotto game; this involves showing how to prune the strategy space to a finite subset before applying our reduction. We also consider the class of dueling games, first introduced by Immorlica et al. (2011). We show that our approach provably extends the class of dueling games for which equilibria can be computed: we introduce a new dueling game, the matching duel, on which prior methods fail to be computationally feasible but upon which our reduction can be applied.

In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes. We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously. We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities.

We propose a new zero-shot Event-Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available modalities from videos into a distributional semantic space. To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) semantic embedding of concepts definitions, and (c) retrieve videos by free text event query (e.g., "changing a vehicle tire") based on their content. We first embed the video into the multi-modal semantic space and then measure the similarity between videos with the event query in free text form. We validated our method on the large TRECVID MED (Multimedia Event Detection) challenge. Using only the event title as a query, our method outperformed the state-the-art that uses big descriptions from 12.6\% to 13.5\% with MAP metric and from 0.73 to 0.83 with ROC-AUC metric. It is also an order of magnitude faster.

A confluence of technical, economic and political forces are revolutionizing the energy sector. Policy-makers, who decide on incentives and penalties for possible courses of actions, play a critical role in determining which outcomes arise. However, designing appropriate energy policies is a complex and challenging task. Our vision is to provide tools and methodologies for policy makers so that they can leverage the power of big data to make evidence-based decisions. In this paper we present an approach we call big-data mechanism design which combines a mechanism design framework with stakeholder surveys and data to allow policy-makers to gauge the costs and benefits of potential policy decisions.We illustrate the effectiveness of this approach in a concrete application domain: the peaksaver PLUS program in Ontario, Canada.

Preference profiles that are single-peaked on trees enjoy desirable properties: they admit a Condorcet winner (Demange 1982), and there are hard voting problems that become tractable on this domain (Yu et al., 2013). Trick (1989) proposed a polynomial-time algorithm that finds some tree with respect to which a given preference profile is single-peaked. However, some voting problems are only known to be easy for profiles that are single-peaked on "nice" trees, and Trick's algorithm provides no guarantees on the properties of the tree that it outputs. To overcome this issue, we build on the work of Trick and Yu et al. to develop a structural approach that enables us to compactly represent all trees with respect to which a given profile is single-peaked. We show how to use this representation to efficiently find the "best" tree for a given profile, according to a number of criteria; for other criteria, we obtain NP-hardness results. In particular, we show that it is NP-hard to decide whether an input profile is single-peaked with respect to a given tree. To demonstrate the applicability of our framework, we use it to identify a new class of profiles that admit an efficient algorithm for a popular variant of the Chamberlin-Courant rule.

Creation of summaries of events of interest from multitude of unstructured data is a challenging task commonly faced by intelligence analysts while seeking increased situational awareness. This paper proposes a framework called Storyboarding that leverages unstructured text and images to explain events as sets of sub-events. The framework first generates a textual context for each human face detected from images and then builds a chain of coherent documents where two consecutive documents of the chain contain a common theme as well as a context. Storyboarding helps analysts quickly narrow down large number of possibilities to a few significant ones for further investigation. Empirical studies on Wikipedia documents, images and news articles show that Storyboarding is able to provide deeper insights on events of interests.

We consider the problem of modeling temporal textual data taking endogenous and exogenous processes into account. Such text documents arise in real world applications, including job advertisements and economic news articles, which are influenced by the fluctuations of the general economy. We propose a hierarchical Bayesian topic model which imposes a "group-correlated" hierarchical structure on the evolution of topics over time incorporating both processes, and show that this model can be estimated from Markov chain Monte Carlo sampling methods. We further demonstrate that this model captures the intrinsic relationships between the topic distribution and the time-dependent factors, and compare its performance with latent Dirichlet allocation (LDA) and two other related models. The model is applied to two collections of documents to illustrate its empirical performance: online job advertisements from DirectEmployers Association and journalists' postings on BusinessInsider.com.

Preventive maintenance is essential for the smooth operation of any equipment. Still, people occasionally do not maintain their equipment adequately. Maintenance alert systems attempt to remind people to perform maintenance. However, most of these systems do not provide alerts at the optimal timing, and nor do they take into account the time required for maintenance or compute the optimal timing for a specific user. We model the problem of maintenance performance, assuming maintenance is time consuming. We solve the optimal policy for the user, i.e., the optimal timing for a user to perform maintenance. This optimal strategy depends on the value of user's time, and thus it may vary from user to user and may change over time. %We present a game Based on the solved optimal strategy we present a personalized maintenance agent, which, depending on the value of user's time, provides alerts to the user when she should perform maintenance. In an experiment using a spaceship computer game, we show that receiving alerts from the personalized alert agent significantly improves user performance.

In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. In principle, zero-shot learning makes it possible to train an event detection model based on the assumption that events (e.g. birthday party) can be described by multiple mid-level semantic concepts (e.g. ``blowing candle'', ``birthday cake''). Towards this goal, we first pre-train a bundle of concept classifiers using data from other sources. Then we evaluate the semantic correlation of each concept w.r.t. the event of interest and pick up the relevant concept classifiers, which are applied on all test videos to get multiple prediction score vectors. While most existing systems combine the predictions of the concept classifiers with fixed weights, we propose to learn the optimal weights of the concept classifiers for each testing video by exploring a set of online available videos with free-form text descriptions of their content. To validate the effectiveness of the proposed approach, we have conducted extensive experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset. The experimental results confirm the superiority of the proposed approach.