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Title: Particle learning of Gaussian process models for sequential design and optimization |
Abstract: We develop a simulation-based method for the online updating of Gaussian process regression and classification models. Our method exploits sequential Monte Carlo to produce a fast sequential design algorithm for these models relative to the established MCMC alternative. The latter is less ideal for sequential... |
Title: Parameter Estimation for Multivariate Diffusion Systems |
Abstract: Diffusion processes are widely used for modelling real-world phenomena. Except for select cases however, analytical expressions do not exist for a diffusion process' transitional probabilities. It is proposed that the cumulant truncation procedure can be applied to predict the evolution of the cumulants of th... |
Title: On the relevance of the Bayesian approach to Statistics |
Abstract: We argue here about the relevance and the ultimate unity of the Bayesian approach in a neutral and agnostic manner. Our main theme is that Bayesian data analysis is an effective tool for handling complex models, as proven by the increasing proportion of Bayesian studies in the applied sciences. We disregard i... |
Title: Errors-in-variables models: a generalized functions approach |
Abstract: Identification in errors-in-variables regression models was recently extended to wide models classes by S. Schennach (Econometrica, 2007) (S) via use of generalized functions. In this paper the problems of non- and semi- parametric identification in such models are re-examined. Nonparametric identification ho... |
Title: Laplacian Support Vector Machines Trained in the Primal |
Abstract: In the last few years, due to the growing ubiquity of unlabeled data, much effort has been spent by the machine learning community to develop better understanding and improve the quality of classifiers exploiting unlabeled data. Following the manifold regularization approach, Laplacian Support Vector Machines... |
Title: Guaranteed Rank Minimization via Singular Value Projection |
Abstract: Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with many important applications in machine learning and statistics. In this paper we propose a simple and fast algorithm SVP (Singular Value Projection) for rank minimization with affine constraints (ARMP) and show that SV... |
Title: Information tracking approach to segmentation of ultrasound imagery of prostate |
Abstract: The size and geometry of the prostate are known to be pivotal quantities used by clinicians to assess the condition of the gland during prostate cancer screening. As an alternative to palpation, an increasing number of methods for estimation of the above-mentioned quantities are based on using imagery data of... |
Title: Iterative Shrinkage Approach to Restoration of Optical Imagery |
Abstract: The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging sciences. In such cases, the degradation is typically caused by the resolution limitations of an imaging device in use and/or by the dest... |
Title: Improvements of the 3D images captured with Time-of-Flight cameras |
Abstract: 3D Time-of-Flight camera's images are affected by errors due to the diffuse (indirect) light and to the flare light. The presented method improves the 3D image reducing the distance's errors to dark surface objects. This is achieved by placing one or two contrast tags in the scene at different distances from ... |
Title: Model choice versus model criticism |
Abstract: The new perspectives on ABC and Bayesian model criticisms presented in Ratmann et al.(2009) are challenging standard approaches to Bayesian model choice. We discuss here some issues arising from the authors' approach, including prior influence, model assessment and criticism, and the meaning of error in ABC. |
Title: Algorithms for finding dispensable variables |
Abstract: This short note reviews briefly three algorithms for finding the set of dispensable variables of a boolean formula. The presentation is light on proofs and heavy on intuitions. |
Title: Finding Associations and Computing Similarity via Biased Pair Sampling |
Abstract: This version is ***superseded*** by a full version that can be found at http://www.itu.dk/people/pagh/papers/mining-jour.pdf, which contains stronger theoretical results and fixes a mistake in the reporting of experiments. Abstract: Sampling-based methods have previously been proposed for the problem of findi... |
Title: Expectation Propagation on the Maximum of Correlated Normal Variables |
Abstract: Many inference problems involving questions of optimality ask for the maximum or the minimum of a finite set of unknown quantities. This technical report derives the first two posterior moments of the maximum of two correlated Gaussian variables and the first two posterior moments of the two generating variab... |
Title: Compressed Blind De-convolution |
Abstract: Suppose the signal x is realized by driving a k-sparse signal u through an arbitrary unknown stable discrete-linear time invariant system H. These types of processes arise naturally in Reflection Seismology. In this paper we are interested in several problems: (a) Blind-Deconvolution: Can we recover both the ... |
Title: Markov Chain Order Estimation and Relative Entropy |
Abstract: We use the $f-divergence$ also called relative entropy as a measure of diversity between probability densities and review its basic properties. In the sequence we define a few objects which capture relevant information from the sample of a Markov Chain to be used in the definition of a couple of estimators i.... |
Title: Post-Processing of Discovered Association Rules Using Ontologies |
Abstract: In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. In this paper we propose a new approach to prune and filter discovered rules. Using Domain Ontologies, we strengthen the integration of user knowledge in the post-processing task. Furthermore, an int... |
Title: Statistical Decision Making for Authentication and Intrusion Detection |
Abstract: User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this problem and propose a novel alternative based on a prin... |
Title: A path algorithm for the Fused Lasso Signal Approximator |
Abstract: The Lasso is a very well known penalized regression model, which adds an $L_1$ penalty with parameter $\lambda_1$ on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an $L_1$ penalty with parameter $\lambda_2$ on the difference of neighboring coefficients... |
Title: A Note On Higher Order Grammar |
Abstract: Both syntax-phonology and syntax-semantics interfaces in Higher Order Grammar (HOG) are expressed as axiomatic theories in higher-order logic (HOL), i.e. a language is defined entirely in terms of provability in the single logical system. An important implication of this elegant architecture is that the meani... |
Title: Pre-processing in AI based Prediction of QSARs |
Abstract: Machine learning, data mining and artificial intelligence (AI) based methods have been used to determine the relations between chemical structure and biological activity, called quantitative structure activity relationships (QSARs) for the compounds. Pre-processing of the dataset, which includes the mapping f... |
Title: Regularization Techniques for Learning with Matrices |
Abstract: There is growing body of learning problems for which it is natural to organize the parameters into matrix, so as to appropriately regularize the parameters under some matrix norm (in order to impose some more sophisticated prior knowledge). This work describes and analyzes a systematic method for constructing... |
Title: Variable sigma Gaussian processes: An expectation propagation perspective |
Abstract: Gaussian processes (GPs) provide a probabilistic nonparametric representation of functions in regression, classification, and other problems. Unfortunately, exact learning with GPs is intractable for large datasets. A variety of approximate GP methods have been proposed that essentially map the large dataset ... |
Title: On the conditions used to prove oracle results for the Lasso |
Abstract: Oracle inequalities and variable selection properties for the Lasso in linear models have been established under a variety of different assumptions on the design matrix. We show in this paper how the different conditions and concepts relate to each other. The restricted eigenvalue condition (Bickel et al., 20... |
Title: Estimating the null distribution for conditional inference and genome-scale screening |
Abstract: In a novel approach to the multiple testing problem, Efron (2004; 2007) formulated estimators of the distribution of test statistics or nominal p-values under a null distribution suitable for modeling the data of thousands of unaffected genes, non-associated single-nucleotide polymorphisms, or other biologica... |
Title: Prediction of Zoonosis Incidence in Human using Seasonal Auto Regressive Integrated Moving Average (SARIMA) |
Abstract: Zoonosis refers to the transmission of infectious diseases from animal to human. The increasing number of zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the future number of zoonosi... |
Title: Reduced-Rank Hidden Markov Models |
Abstract: We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-concave predictive distributions as in continuous-observation HMMs. RR-HMMs assume an m-dimensional latent state and n discrete ob... |
Title: Low-rank Matrix Completion with Noisy Observations: a Quantitative Comparison |
Abstract: We consider a problem of significant practical importance, namely, the reconstruction of a low-rank data matrix from a small subset of its entries. This problem appears in many areas such as collaborative filtering, computer vision and wireless sensor networks. In this paper, we focus on the matrix completion... |
Title: BRAINSTORMING: Consensus Learning in Practice |
Abstract: We present here an introduction to Brainstorming approach, that was recently proposed as a consensus meta-learning technique, and used in several practical applications in bioinformatics and chemoinformatics. The consensus learning denotes heterogeneous theoretical classification method, where one trains an e... |
Title: Functional learning through kernels |
Abstract: This paper reviews the functional aspects of statistical learning theory. The main point under consideration is the nature of the hypothesis set when no prior information is available but data. Within this framework we first discuss about the hypothesis set: it is a vectorial space, it is a set of pointwise d... |
Title: Building upon Fast Multipole Methods to Detect and Model Organizations |
Abstract: Many models in natural and social sciences are comprised of sets of inter-acting entities whose intensity of interaction decreases with distance. This often leads to structures of interest in these models composed of dense packs of entities. Fast Multipole Methods are a family of methods developed to help wit... |
Title: Distance Dependent Chinese Restaurant Processes |
Abstract: We develop the distance dependent Chinese restaurant process (CRP), a flexible class of distributions over partitions that allows for non-exchangeability. This class can be used to model many kinds of dependencies between data in infinite clustering models, including dependencies across time or space. We exam... |
Title: A multiagent urban traffic simulation. Part II: dealing with the extraordinary |
Abstract: In Probabilistic Risk Management, risk is characterized by two quantities: the magnitude (or severity) of the adverse consequences that can potentially result from the given activity or action, and by the likelihood of occurrence of the given adverse consequences. But a risk seldom exists in isolation: chain ... |
Title: Time-varying Coefficients Estimation in Differential Equation Models with Noisy Time-varying Covariates |
Abstract: We study the problem of estimating time-varying coefficients in ordinary differential equations. Current theory only applies to the case when the associated state variables are observed without measurement errors as presented in . The difficulty arises from the quadratic functional of observations that one ne... |
Title: A Local Search Modeling for Constrained Optimum Paths Problems (Extended Abstract) |
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