Urban mobility is not only one of the pillars of modern economic systems, but also a key issue in the quest for equality of opportunity, once it can improve access to other services. Currently, however, there are a number of negative issues related to traffic, especially in mega-cities, such as economical issues (cost of opportunity caused by delays), environmental (externalities related to emissions of pollutants), and social (traffic accidents). Solutions to these issues are more and more closely tied to information and communication technology. Indeed, a search in the technical literature (using the keyword urban traffic" to filter out articles on data network traffic) retrieved the following number of articles (as of December 3, 2013): 9,443 (ACM Digital Library), 26,054 (Scopus), and 1,730,000 (Google Scholar). Moreover, articles listed in the ACM query relate to conferences as diverse as MobiCom, CHI, PADS, and AAMAS.

Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This paper provides a gentle introduction to non-parametric Bayesian modeling of complex networks: Using an infinite mixture model as running example we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model's fit and predictive performance. We explain how advanced non-parametric models for complex networks can be derived and point out relevant literature.

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education.

Unfortunately, real-world datasets often include only samples of observed values mixed with many missing or unknown elements. Missing values may occur due to human impatience, human error during data entry, data loss, faulty sensory equipment, changes in data collection methods, inability to decipher handwriting, privacy issues, legal requirements, and a variety of other practical factors. Thus, improvements to methods for imputing missing values can have far-reaching impact on improving the effectiveness of existing learning algorithms for operating on real-world data. We present a method for imputation called Unsupervised Backpropagation (UBP), which trains a multilayer perceptron (MLP) to fit to the manifold represented by the known features in a dataset. We demonstrate this algorithm with the task of imputing missing values, and we show that it is significantly more effective than other methods for imputation. Backpropagation has long been a popular method for training neural networks (Rumelhart et al., 1986; Werbos, 1990).

Probabilistic models play a central role in modern machine learning. They offer a powerful framework for learning from data, and they have found applications in a variety of scientific fields beyond machine learning. A longstanding goal in machine learning and statistics is to achieve a separation between modeling and inference, so that users of these tools may focus on specifying models without having to implement new inference algorithms every time the models change. Recently, work in probabilistic programming has taken up this challenge, seeking to unify probabilistic modeling with computer programming in order to dramatically increase the effectiveness of machine learning experts (DARPA, 2013) and to equip non-experts with effective tools for specifying models and performing inference. We anticipate that continued success toward these goals will decrease the reliance of machine learning practitioners on tried-and-true models and will shift the community toward a paradigm grounded in flexible tools for rapidly prototyping and designing new models (Bishop, 2013).

Systematic, or market, risk is one of the most studied risk models not only in financial engineering, but also in actuarial sciences, in business and corporate management, and in several other domains. It is associated to the beta (β) coefficient, which is familiar in the investment industry since Sharpe's capital asset pricing model (CAPM) [30]. The pitfalls and shortcomings of β have been detailed by a number of excellent authors.

Functional Classification, i.e. the binary classification problem when the input observation X (X(t)) is of the form of a (possibly sampled) random curve/function and the output variable Y { 1, 1} is a binary label, has been the subject of a good deal of attention in the machine-learning literature in the past few years, see [1] or [2]. In contrast, Bipartite Ranking, termed Nonparametric Scoring sometimes, has never been tackled in a functional framework, except from the restrictive angle of Functional Logistic Regression, see [3] or [4] for instance. This global learning task consists in ordering all possible input observations X so that positive ones appear on top of the list with highest probability. This predictive problem, which can be cast in terms of ROC curve optimization (see [5]), covers a wide variety of applications, ranging from anomaly detection in signal processing to automatic design of diagnosis tools in medicine through creditscoring in mathematical finance or the conception of search engines in information retrieval. Functional versions of many popular approaches for classification have been developed, relying in general on a preliminary finite dimensional representation/projection of the input data.

The goal of supervised statistical learning is to develop prediction rules taking the values of predictor variables as input and returning a predicted value of the response variable. A prediction rule is typically learnt by applying a learning algorithm M to a so-called learning data set. A typical example in biomedical research is the prediction of patient outcome (e.g. The practitioners are usually interested in the accuracy of the prediction rule learnt from their data set to predict future patients, while methodological researchers rather want to know whether the learning algorithm is good at learning accurate prediction rules for different data sets drawn from a distribution of interest. The first perspective is called "conditional" (since referring to a specific data set) while the latter, which we take in this paper, is denoted as "unconditional". If the data set is very large, one can observe independent realizations of estimators of the unconditional error rates and use them for a paired t-test (see Section 2.3). In practise, however, huge data sets are rarely available. Prediction errors are thus usually estimated by resampling procedures consisting of splitting the available data set into learning and test sets a large number of times and averaging the estimated error over these iterations.

We study the representation of systems S of linear equations over the two-element field (aka xor- or parity-constraints) via conjunctive normal forms F (boolean clause-sets). First we consider the problem of finding an "arc-consistent" representation ("AC"), meaning that unit-clause propagation will fix all forced assignments for all possible instantiations of the xor-variables. Our main negative result is that there is no polysize AC-representation in general. On the positive side we show that finding such an AC-representation is fixed-parameter tractable (fpt) in the number of equations. Then we turn to a stronger criterion of representation, namely propagation completeness ("PC") --- while AC only covers the variables of S, now all the variables in F (the variables in S plus auxiliary variables) are considered for PC. We show that the standard translation actually yields a PC representation for one equation, but fails so for two equations (in fact arbitrarily badly). We show that with a more intelligent translation we can also easily compute a translation to PC for two equations. We conjecture that computing a representation in PC is fpt in the number of equations.

We study the phase transition of the coalitional manipulation problem for generalized scoring rules. Previously it has been shown that, under some conditions on the distribution of votes, if the number of manipulators is o(sqrt{n}), where n is the number of voters, then the probability that a random profile is manipulable by the coalition goes to zero as the number of voters goes to infinity, whereas if the number of manipulators is omega(sqrt{n}), then the probability that a random profile is manipulable goes to one. Here we consider the critical window, where a coalition has size c*sqrt{n}, and we show that as c goes from zero to infinity, the limiting probability that a random profile is manipulable goes from zero to one in a smooth fashion, i.e., there is a smooth phase transition between the two regimes. This result analytically validates recent empirical results, and suggests that deciding the coalitional manipulation problem may be of limited computational hardness in practice.