Understanding the demographics of users of online social networks has important applications for health, marketing, and public messaging. In this paper, we predict the demographics of Twitter users based on whom they follow. Whereas most prior approaches rely on a supervised learning approach, in which individual users are labeled with demographics, we instead create a distantly labeled dataset by collecting audience measurement data for 1,500 websites (e.g., 50% of visitors to gizmodo.com are estimated to have a bachelor's degree). We then fit a regression model to predict these demographics using information about the followers of each website on Twitter. The resulting average held-out correlation is .77 across six different variables (gender, age, ethnicity, education, income, and child status). We additionally validate the model on a smaller set of Twitter users labeled individually for ethnicity and gender, finding performance that is surprisingly competitive with a fully supervised approach.

Group sparsity has drawn much attention in machine learning. However, existing work can handle only datasets with certain group structures, where each sample has a certain membership with one or more groups. This paper investigates the learning of sparse representations from datasets with uncertain group structures, where each sample has an uncertain member-ship with all groups in terms of a probability distribution. We call this problem uncertain group sparse representation (UGSR in short), which is a generalization of the standard group sparse representation (GSR). We formulate the UGSR model and propose an efficient algorithm to solve this problem. We apply UGSR to text emotion classification and aging face recognition. Experiments show that UGSR outperforms standard sparse representation (SR) and standard GSR as well as fuzzy kNN classification.

The abundance of algorithms developed to solve different problems has given rise to an important research question: How do we choose the best algorithm for a given problem? Known as algorithm selection, this issue has been prevailing in many domains, as no single algorithm can perform best on all problem instances. Traditional algorithm selection and portfolio construction methods typically treat the problem as a classification or regression task. In this paper, we present a new approach that provides a more natural treatment of algorithm selection and portfolio construction as a ranking task. Accordingly, we develop a Ranking-Based Algorithm Selection (RAS) method, which employs a simple polynomial model to capture the ranking of different solvers for different problem instances. We devise an efficient iterative algorithm that can gracefully optimize the polynomial coefficients by minimizing a ranking loss function, which is derived from a sound probabilistic formulation of the ranking problem. Experiments on the SAT 2012 competition dataset show that our approach yields competitive performance to that of more sophisticated algorithm selection methods.

AI planning techniques often require a given set of action models provided as input. Creating action models is, however, a difficult task that costs much manual effort. The problem of action-model acquisition has drawn a lot of interest from researchers in the past. Despite the success of the previous systems, they are all based on the assumption that there are enough training examples for learning high-quality action models. In many real-world applications, e.g., military operation, collecting a large amount of training examples is often both difficult and costly. Instead of collecting training examples, we assume there are abundant annotators, i.e., the crowd, available to provide information learning action models. Specifically, we first build a set of soft constraints based on the labels (true or false) given by the crowd or annotators. We then builds a set of soft constraints based on the input plan traces. After that we put all the constraints together and solve them using a weighted MAX-SAT solver, and convert the solution of the solver to action models. We finally exhibit that our approach is effective in the experiment.

Knowledge compilation is a powerful reasoning paradigm with many applications across AI and computer science more broadly. We consider the problem of bottom-up compilation of knowledge bases, which is usually predicated on the existence of a polytime function for combining compilations using Boolean operators (usually called an Apply function). While such a polytime Apply function is known to exist for certain languages (e.g., OBDDs) and not exist for others (e.g., DNNFs), its existence for certain languages remains unknown. Among the latter is the recently introduced language of Sentential Decision Diagrams (SDDs): while a polytime Apply function exists for SDDs, it was unknown whether such a function exists for the important subset of compressed SDDs which are canonical. We resolve this open question in this paper and consider some of its theoretical and practical implications. Some of the findings we report question the common wisdom on the relationship between bottom-up compilation, language canonicity and the complexity of the Apply function.

Linear discriminant analysis (LDA) is a popular dimensionality reduction and classification method that simultaneously maximizes between-class scatter and minimizes within-class scatter. In this paper, we verify the equivalence of LDA and least squares (LS) with a set of dependent variable matrices. The equivalence is in the sense that the LDA solution matrix and the LS solution matrix have the same range. The resulting LS provides an intuitive interpretation in which its solution performs data clustering according to class labels. Further, the fact that LDA and LS have the same range allows us to design a two-stage algorithm that computes the LDA solution given by generalized eigenvalue decomposition (GEVD), much faster than computing the original GEVD. Experimental results demonstrate the equivalence of the LDA solution and the proposed LS solution.

Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.

The rapid urban expansion has greatly extended the physical boundary of users' living area and developed a large number of POIs (points of interest). POI recommendation is a task that facilitates users' urban exploration and helps them filter uninteresting POIs for decision making. While existing work of POI recommendation on location-based social networks (LBSNs) discovers the spatial, temporal, and social patterns of user check-in behavior, the use of content information has not been systematically studied. The various types of content information available on LBSNs could be related to different aspects of a user's check-in action, providing a unique opportunity for POI recommendation. In this work, we study the content information on LBSNs w.r.t. POI properties, user interests, and sentiment indications. We model the three types of information under a unified POI recommendation framework with the consideration of their relationship to check-in actions. The experimental results exhibit the significance of content information in explaining user behavior, and demonstrate its power to improve POI recommendation performance on LBSNs.

Image clustering and visual codebook learning are two fundamental problems in computer vision and they are tightly related. On one hand, a good codebook can generate effective feature representations which largely affect clustering performance. On the other hand, class labels obtained from image clustering can serve as supervised information to guide codebook learning. Traditionally, these two processes are conducted separately and their correlation is generally ignored.In this paper, we propose a Double Layer Gaussian Mixture Model (DLGMM) to simultaneously perform image clustering and codebook learning. In DLGMM, two tasks are seamlessly coupled and can mutually promote each other. Cluster labels and codebook are jointly estimated to achieve the overall best performance. To incorporate the spatial coherence between neighboring visual patches, we propose a Spatially Coherent DLGMM which uses a Markov Random Field to encourage neighboring patches to share the same visual word label.We use variational inference to approximate the posterior of latent variables and learn model parameters.Experiments on two datasets demonstrate the effectiveness of two models.

Stock movements are essentially driven by new information. Market data, financial news, and social sentiment are believed to have impacts on stock markets. To study the correlation between information and stock movements, previous works typically concatenate the features of different information sources into one super feature vector. However, such concatenated vector approaches treat each information source separately and ignore their interactions. In this article, we model the multi-faceted investors’ information and their intrinsic links with tensors. To identify the nonlinear patterns between stock movements and new information, we propose a supervised tensor regression learning approach to investigate the joint impact of different information sources on stock markets. Experiments on CSI 100 stocks in the year 2011 show that our approach outperforms the state-of-the-art trading strategies.