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Title: A Primal-Dual Convergence Analysis of Boosting |
Abstract: Boosting combines weak learners into a predictor with low empirical risk. Its dual constructs a high entropy distribution upon which weak learners and training labels are uncorrelated. This manuscript studies this primal-dual relationship under a broad family of losses, including the exponential loss of AdaBo... |
Title: Using Feature Weights to Improve Performance of Neural Networks |
Abstract: Different features have different relevance to a particular learning problem. Some features are less relevant; while some very important. Instead of selecting the most relevant features using feature selection, an algorithm can be given this knowledge of feature importance based on expert opinion or prior lea... |
Title: A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation |
Abstract: Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive... |
Title: The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures |
Abstract: Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing fe... |
Title: A Novel Template-Based Learning Model |
Abstract: This article presents a model which is capable of learning and abstracting new concepts based on comparing observations and finding the resemblance between the observations. In the model, the new observations are compared with the templates which have been derived from the previous experiences. In the first s... |
Title: Geometric representations for minimalist grammars |
Abstract: We reformulate minimalist grammars as partial functions on term algebras for strings and trees. Using filler/role bindings and tensor product representations, we construct homomorphisms for these data structures into geometric vector spaces. We prove that the structure-building functions as well as simple pro... |
Title: Why approximate Bayesian computational (ABC) methods cannot handle model choice problems |
Abstract: Approximate Bayesian computation (ABC), also known as likelihood-free methods, have become a favourite tool for the analysis of complex stochastic models, primarily in population genetics but also in financial analyses. We advocated in Grelaud et al. (2009) the use of ABC for Bayesian model choice in the spec... |
Title: Infinite Multiple Membership Relational Modeling for Complex Networks |
Abstract: Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to existing multiple-... |
Title: A Complex Networks Approach for Data Clustering |
Abstract: Many methods have been developed for data clustering, such as k-means, expectation maximization and algorithms based on graph theory. In this latter case, graphs are generally constructed by taking into account the Euclidian distance as a similarity measure, and partitioned using spectral methods. However, th... |
Title: Bayesian Network Structure Learning with Permutation Tests |
Abstract: In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learning algorithms are based on, i.e. the maximisation algorithms (in score-based algorithms) or the techniques for learning the depende... |
Title: A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity |
Abstract: The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions ... |
Title: An Analysis of the Convergence of Graph Laplacians |
Abstract: Existing approaches to analyzing the asymptotics of graph Laplacians typically assume a well-behaved kernel function with smoothness assumptions. We remove the smoothness assumption and generalize the analysis of graph Laplacians to include previously unstudied graphs including kNN graphs. We also introduce a... |
Title: A Human-Centric Approach to Group-Based Context-Awareness |
Abstract: The emerging need for qualitative approaches in context-aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoret... |
Title: Walking on a Graph with a Magnifying Glass: Stratified Sampling via Weighted Random Walks |
Abstract: Our objective is to sample the node set of a large unknown graph via crawling, to accurately estimate a given metric of interest. We design a random walk on an appropriately defined weighted graph that achieves high efficiency by preferentially crawling those nodes and edges that convey greater information re... |
Title: Developing a New Approach for Arabic Morphological Analysis and Generation |
Abstract: Arabic morphological analysis is one of the essential stages in Arabic Natural Language Processing. In this paper we present an approach for Arabic morphological analysis. This approach is based on Arabic morphological automaton (AMAUT). The proposed technique uses a morphological database realized using XMOD... |
Title: Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing |
Abstract: Recent research in multi-robot exploration and mapping has focused on sampling environmental fields, which are typically modeled using the Gaussian process (GP). Existing information-theoretic exploration strategies for learning GP-based environmental field maps adopt the non-Markovian problem structure and c... |
Title: On the Local Correctness of L^1 Minimization for Dictionary Learning |
Abstract: The idea that many important classes of signals can be well-represented by linear combinations of a small set of atoms selected from a given dictionary has had dramatic impact on the theory and practice of signal processing. For practical problems in which an appropriate sparsifying dictionary is not known ah... |
Title: A correspondence-less approach to matching of deformable shapes |
Abstract: Finding a match between partially available deformable shapes is a challenging problem with numerous applications. The problem is usually approached by computing local descriptors on a pair of shapes and then establishing a point-wise correspondence between the two. In this paper, we introduce an alternative ... |
Title: Recursive $\ell_1,\infty$ Group lasso |
Abstract: We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal $\ell_1,\infty$-penalized recursive least sq... |
Title: Polarized Montagovian Semantics for the Lambek-Grishin calculus |
Abstract: Grishin proposed enriching the Lambek calculus with multiplicative disjunction (par) and coresiduals. Applications to linguistics were discussed by Moortgat, who spoke of the Lambek-Grishin calculus (LG). In this paper, we adapt Girard's polarity-sensitive double negation embedding for classical logic to extr... |
Title: Geometric Models with Co-occurrence Groups |
Abstract: A geometric model of sparse signal representations is introduced for classes of signals. It is computed by optimizing co-occurrence groups with a maximum likelihood estimate calculated with a Bernoulli mixture model. Applications to face image compression and MNIST digit classification illustrate the applicab... |
Title: Statistical Compressed Sensing of Gaussian Mixture Models |
Abstract: A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution, and achieving accurate reconstruction on average, is introduced. SCS based on Gaussian models is investigated in depth. For si... |
Title: The VC-Dimension of Queries and Selectivity Estimation Through Sampling |
Abstract: We develop a novel method, based on the statistical concept of the Vapnik-Chervonenkis dimension, to evaluate the selectivity (output cardinality) of SQL queries - a crucial step in optimizing the execution of large scale database and data-mining operations. The major theoretical contribution of this work, wh... |
Title: Nonasymptotic bounds on the mean square error for MCMC estimates via renewal techniques |
Abstract: The Nummellin's split chain construction allows to decompose a Markov chain Monte Carlo (MCMC) trajectory into i.i.d. "excursions". RegenerativeMCMC algorithms based on this technique use a random number of samples. They have been proposed as a promising alternative to usual fixed length simulation [25, 33, 1... |
Title: Adaptive Gibbs samplers and related MCMC methods |
Abstract: We consider various versions of adaptive Gibbs and Metropolis-within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the fly during a run by learning as they go in an attempt to optimize the algorithm. We present a cautionary example of how even a ... |
Title: Dependency detection with similarity constraints |
Abstract: Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant ... |
Title: Boolean network robotics: a proof of concept |
Abstract: Dynamical systems theory and complexity science provide powerful tools for analysing artificial agents and robots. Furthermore, they have been recently proposed also as a source of design principles and guidelines. Boolean networks are a prominent example of complex dynamical systems and they have been shown ... |
Title: Solving the Satisfiability Problem Through Boolean Networks |
Abstract: In this paper we present a new approach to solve the satisfiability problem (SAT), based on boolean networks (BN). We define a mapping between a SAT instance and a BN, and we solve SAT problem by simulating the BN dynamics. We prove that BN fixed points correspond to the SAT solutions. The mapping presented a... |
Title: Sequential Monte Carlo on large binary sampling spaces |
Abstract: A Monte Carlo algorithm is said to be adaptive if it automatically calibrates its current proposal distribution using past simulations. The choice of the parametric family that defines the set of proposal distributions is critical for good performance. In this paper, we present such a parametric family for ad... |
Title: Spatially-Aware Comparison and Consensus for Clusterings |
Abstract: This paper proposes a new distance metric between clusterings that incorporates information about the spatial distribution of points and clusters. Our approach builds on the idea of a Hilbert space-based representation of clusters as a combination of the representations of their constituent points. We use thi... |
Title: Smart depth of field optimization applied to a robotised view camera |
Abstract: The great flexibility of a view camera allows to take high quality photographs that would not be possible any other way. But making a given object into focus is a long and tedious task, although the underlying laws are well known. This paper presents the result of a project which has lead to the design of a c... |
Title: Statistical methods for tissue array images - algorithmic scoring and co-training |
Abstract: Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive lim... |
Title: Vector Diffusion Maps and the Connection Laplacian |
Abstract: We introduce \em vector diffusion maps (VDM), a new mathematical framework for organizing and analyzing massive high dimensional data sets, images and shapes. VDM is a mathematical and algorithmic generalization of diffusion maps and other non-linear dimensionality reduction methods, such as LLE, ISOMAP and L... |
Title: Information-theoretic measures associated with rough set approximations |
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