An appealing alternative to tediously specifying robot behaviors in response to particular image features is to have the robot’s behavior be induced by human decisions made when piloting the robot. This paper presents one promising approach to creating this alternative. A human pilots a camera-equipped robot, which builds a representation of its target environment using Growing Neural Gas (GNG). The robot associates an action with each GNG node based on what the human pilot was doing while the node was active. When running autonomously, the robot chooses the action associated with the node that is the closest match to the current input image. Preliminary results suggest that the approach has potential, but that subsequent alteration of the actions induced for some of the GNG nodes is important for acceptable performance.

Using geospatial methods, this paper informs the evolving field of research on the diffusion of residential Third Party Owned PV systems by analyzing 1) the spatial distribution of TPO systems, and 2) the influence of demographics on the adoption on the local level. This research is part of a multidisciplinary study into the diffusion of solar technology (SEEDS), using San Diego County as focus area. Our findings reveal a significant clustering of TPO PV adoption in San Diego County. TPO systems reached a similarly high market share across a large area in the central county in contrast to the installation of host-owned systems, which have been less evenly distributed across single-family households in the same area. The diffusion of TPO systems in San Diego County can be partially explained by looking at median income and percentage of people born in the US. The explanatory power of the model varies across the region.

Motivated by generating personalized recommendations using ordinal (or preference) data, we study the question of learning a mixture of MultiNomial Logit (MNL) model, a parameterized class of distributions over permutations, from partial ordinal or preference data (e.g. pair-wise comparisons). Despite its long standing importance across disciplines including social choice, operations research and revenue management, little is known about this question. In case of single MNL models (no mixture), computationally and statistically tractable learning from pair-wise comparisons is feasible. However, even learning mixture with two MNL components is infeasible in general. Given this state of affairs, we seek conditions under which it is feasible to learn the mixture model in both computationally and statistically efficient manner. We present a sufficient condition as well as an efficient algorithm for learning mixed MNL models from partial preferences/comparisons data. In particular, a mixture of $r$ MNL components over $n$ objects can be learnt using samples whose size scales polynomially in $n$ and $r$ (concretely, $r^{3.5}n^3(log n)^4$, with $r\ll n^{2/7}$ when the model parameters are sufficiently incoherent). The algorithm has two phases: first, learn the pair-wise marginals for each component using tensor decomposition; second, learn the model parameters for each component using Rank Centrality introduced by Negahban et al. In the process of proving these results, we obtain a generalization of existing analysis for tensor decomposition to a more realistic regime where only partial information about each sample is available.

Crowdsourcing is a popular paradigm for effectively collecting labels at low cost. The Dawid-Skene estimator has been widely used for inferring the true labels from the noisy labels provided by non-expert crowdsourcing workers. However, since the estimator maximizes a non-convex log-likelihood function, it is hard to theoretically justify its performance. In this paper, we propose a two-stage efficient algorithm for multi-class crowd labeling problems. The first stage uses the spectral method to obtain an initial estimate of parameters. Then the second stage refines the estimation by optimizing the objective function of the Dawid-Skene estimator via the EM algorithm. We show that our algorithm achieves the optimal convergence rate up to a logarithmic factor. We conduct extensive experiments on synthetic and real datasets. Experimental results demonstrate that the proposed algorithm is comparable to the most accurate empirical approach, while outperforming several other recently proposed methods.

In signal analysis and synthesis, linear approximation theory considers a linear decomposition of any given signal in a set of atoms, collected into a so-called dictionary. Relevant sparse representations are obtained by relaxing the orthogonality condition of the atoms, yielding overcomplete dictionaries with an extended number of atoms. More generally than the linear decomposition, overcomplete kernel dictionaries provide an elegant nonlinear extension by defining the atoms through a mapping kernel function (e.g., the gaussian kernel). Models based on such kernel dictionaries are used in neural networks, gaussian processes and online learning with kernels. The quality of an overcomplete dictionary is evaluated with a diversity measure the distance, the approximation, the coherence and the Babel measures. In this paper, we develop a framework to examine overcomplete kernel dictionaries with the entropy from information theory. Indeed, a higher value of the entropy is associated to a further uniform spread of the atoms over the space. For each of the aforementioned diversity measures, we derive lower bounds on the entropy. Several definitions of the entropy are examined, with an extensive analysis in both the input space and the mapped feature space.

One of the most popular uses of crowdsourcing is to provide training data for supervised machine learning algorithms. Since human annotators often make errors, requesters commonly ask multiple workers to label each example.  But is this strategy always the most cost effective use of crowdsourced workers? We argue "No" --- often classifiers can achieve higher accuracies when trained with noisy "unilabeled" data. However, in some cases relabeling is extremely important.  We discuss three factors that may make relabeling an effective strategy: classifier expressiveness, worker accuracy, and budget.

While temporal behavioral patterns can be discerned to underlie real crowd work, prior studies have typically modeled worker performance under a simplified i.i.d. assumption. To better model such temporal worker behavior, we propose a time-series label prediction model for crowd work. This latent variable model captures and summarizes past worker behavior, enabling us to better predict the quality of each worker's next label. Given inherent uncertainty in prediction, we also investigate a decision reject option to balance the tradeoff between prediction accuracy vs. coverage. Results show our model improves accuracy of both label prediction on real crowd worker data, as well as data quality overall.

A product concept evaluation system combining conjoint analysis with prediction markets is developed. It is also proposed how to determine the payoff for each prediction security corresponding to a product concept, so as to have participants to behave truthfully in the market. Further, how the proposed system works is investigated by evolutionary game simulation.

Quality control is one of the central issues in crowdsourcing research. In this paper, we consider a quality control problem of crowdsourced enumeration tasks that request workers to enumerate possible answers as many as possible. Since workers neither necessarily provide correct answers nor provide exactly the same answers even if the answers indicate the same idea, we propose a two-stage quality control method consisting of the answer clustering stage and the reliability estimation stage.

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.