The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like renewable energy. The model consists of several sophisticated and established approaches and can be regarded as a periodic VAR-TARCH with wind power, solar power, and load as influences on the time series. It is able to map the distinct and well-known features of electricity prices in Germany. An efficient iteratively reweighted lasso approach is used for the estimation. Moreover, it is shown that several existing models are outperformed by the procedure developed in this paper.

This work introduces an integrative approach based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The objective of the application of neural hyper-network to pulmonary embolism (PE) is to improve diagnose for reducing the number of CT-angiography needed. Hypernetworks, based on topological simplicial complex, generalize the concept of two-relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. Another important results is that Q-analysis stays close to the data, while other approaches manipulate data, projecting them into metric spaces or applying some filtering functions to highlight the intrinsic relations. A pulmonary embolism (PE) is a blockage of the main artery of the lung or one of its branches, frequently fatal. Our study uses data on 28 diagnostic features of 1,427 people considered to be at risk of PE. The resulting neural hypernetwork correctly recognized 94% of those developing a PE. This is better than previous results that have been obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space).

Solving different types of optimization models (including parameters fitting) for support vector machines on large-scale training data is often an expensive computational task. This paper proposes a multilevel algorithmic framework that scales efficiently to very large data sets. Instead of solving the whole training set in one optimization process, the support vectors are obtained and gradually refined at multiple levels of coarseness of the data. The proposed framework includes: (a) construction of hierarchy of large-scale data coarse representations, and (b) a local processing of updating the hyperplane throughout this hierarchy. Our multilevel framework substantially improves the computational time without loosing the quality of classifiers. The algorithms are demonstrated for both regular and weighted support vector machines. Experimental results are presented for balanced and imbalanced classification problems. Quality improvement on several imbalanced data sets has been observed.

We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains.

Mass spectrometry can involve two soft ionization techniques: matrix-assisted laser desorption ionization (MALDI) and surface-enhanced laser desorption and ionization (SELDI). For each analyzed fluid sample, MALDI or SELDI hardwares generate a high-dimensional mass spectrum, recording between 10,000 and 20,000 "mass-to-charge (m/z) ratios" corresponding to the ionized peptides present in the fluid sample, as well as "intensities" roughly quantifying the concentrations of these peptides in the sample. Generally m/z ratios take values anywhere between 200 and 20,000 Daltons, and are acquired with a known relative accuracy ρ which depends on the acquisition modalities, and ranges from 0.1% to 0.3%. Analyzing this type of high dimensional data oftern requires specialized software tools, implementing sophisticated machine learning techniques such as SVM (support vector machines) (Li and others (2004), Yu and others (2005)), artificial neural networks (Ball and others (2002)), or random forests (Izmirlian (2004)). These techniques typically generate "black-box" classifiers, which often reach good discrimination levels between cancerous and control groups, but are difficult to interpret biologically in terms of characteristic biomarkers patterns. This often leads to unexpected performance variations on totally new data sets. To develop clinically usable software tools for analysis of mass spectra acuired by MALDI or SELDI hardwares, a key step is to implement automated discovery of explicit "signatures", i.e. short lists of proteomic biomarkers with high discriminating powers between cancer groups (Yasui and others (2003)). Some easily interpretable automatic classifiers, such as linear combinations of biomarker weights (Wang and Chang (2011)), can be found in previous studies, but these approaches do not attempt to quantify the discriminating impact of simultaneous presence for specific pairs of biomarkers.

We study prediction and estimation problems using empirical risk minimization, relative to a general convex loss function. We obtain sharp error rates even when concentration is false or is very restricted, for example, in heavy-tailed scenarios. Our results show that the error rate depends on two parameters: one captures the intrinsic complexity of the class, and essentially leads to the error rate in a noise-free (or realizable) problem; the other measures interactions between class members the target and the loss, and is dominant when the problem is far from realizable. We also explain how one may deal with outliers by choosing the loss in a way that is calibrated to the intrinsic complexity of the class and to the noise-level of the problem (the latter is measured by the distance between the target and the class).

We develop a scoring and classification procedure based on the PAC-Bayesian approach and the AUC (Area Under Curve) criterion. We focus initially on the class of linear score functions. We derive PAC-Bayesian non-asymptotic bounds for two types of prior for the score parameters: a Gaussian prior, and a spike-and-slab prior; the latter makes it possible to perform feature selection. One important advantage of our approach is that it is amenable to powerful Bayesian computational tools. We derive in particular a Sequential Monte Carlo algorithm, as an efficient method which may be used as a gold standard, and an Expectation-Propagation algorithm, as a much faster but approximate method. We also extend our method to a class of non-linear score functions, essentially leading to a nonparametric procedure, by considering a Gaussian process prior.

In the contextual bandit problem, an agent collects rewards for actions taken over a sequence of rounds; in each round, the agent chooses an action to take on the basis of (i) context (or features) for the current round, as well as (ii) feedback, in the form of rewards, obtained in previous rounds. The feedback is incomplete: in any given round, the agent observes the reward only for the chosen action; the agent does not observe the reward for other actions. Contextual bandit problems are found in many important applications such as online recommendation and clinical trials, and represent a natural halfway point between supervised learning and reinforcement learning. The use of features to encode context is inherited from supervised machine learning, while exploration is necessary for good performance as in reinforcement learning. The choice of exploration distribution on actions is important. The strongest known results(Auer et al., 2002; McMahan and Streeter, 2009; Beygelzimer et al., 2011) provide algorithms that carefully control the exploration distribution to achieve an optimal regret after T rounds of () O KT log( Π /δ), with probability at least 1 δ, relative to a set of policies Π A

Neural population activity often exhibits rich variability and temporal structure. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as "non-stationarity". To better understand the nature of co-variability in neural circuits and their impact on cortical information processing, we need statistical models that are able to capture multiple sources of variability on different time-scales. Here, we introduce a hierarchical statistical model of neural population activity which models both neural population dynamics as well as inter-trial modulations in firing rates. In addition, we extend the model to allow us to capture non-stationarities in the population dynamics itself (i.e., correlations across neurons). We develop variational inference methods for learning model parameters, and demonstrate that the method can recover non-stationarities in both average firing rates and correlation structure. Applied to neural population recordings from anesthetized macaque primary visual cortex, our models provide a better account of the structure of neural firing than stationary dynamics models.

The goal of this paper is to introduce a preliminary version of what we call multi-scale local shape analysis (MLSA), a method for extracting features of a dataset that describe the local structure, both manifold and singular, of points within the dataset. MLSA is a mixture of multi-scale local principal component analysis (MLPCA) and persistent local homology (PLH). In this paper, we will describe both of these techniques and our merger of them, and we will demonstrate the potential of MLSA on two synthetic datasets and one real one. The potential of these methods and their merger is investigated in the context of one of the typical applications for data analytics: the classification problem for multidimensional datasets. Thus the relevance of the developed techniques is assessed as the quality of the resulting classification decision rule, measured by the expected test misclassification error, its sensitivity and specificity (false positive and false negative error rates).