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Title: Learning Latent Tree Graphical Models |
Abstract: We study the problem of learning a latent tree graphical model where samples are available only from a subset of variables. We propose two consistent and computationally efficient algorithms for learning minimal latent trees, that is, trees without any redundant hidden nodes. Unlike many existing methods, the... |
Title: Invariant Spectral Hashing of Image Saliency Graph |
Abstract: Image hashing is the process of associating a short vector of bits to an image. The resulting summaries are useful in many applications including image indexing, image authentication and pattern recognition. These hashes need to be invariant under transformations of the image that result in similar visual con... |
Title: Power Euclidean metrics for covariance matrices with application to diffusion tensor imaging |
Abstract: Various metrics for comparing diffusion tensors have been recently proposed in the literature. We consider a broad family of metrics which is indexed by a single power parameter. A likelihood-based procedure is developed for choosing the most appropriate metric from the family for a given dataset at hand. The... |
Title: Bayesian matching of unlabelled point sets using Procrustes and configuration models |
Abstract: The problem of matching unlabelled point sets using Bayesian inference is considered. Two recently proposed models for the likelihood are compared, based on the Procrustes size-and-shape and the full configuration. Bayesian inference is carried out for matching point sets using Markov chain Monte Carlo simula... |
Title: Asymmetric Totally-corrective Boosting for Real-time Object Detection |
Abstract: Real-time object detection is one of the core problems in computer vision. The cascade boosting framework proposed by Viola and Jones has become the standard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate fa... |
Title: Intrinsic Inference on the Mean Geodesic of Planar Shapes and Tree Discrimination by Leaf Growth |
Abstract: For planar landmark based shapes, taking into account the non-Euclidean geometry of the shape space, a statistical test for a common mean first geodesic principal component (GPC) is devised. It rests on one of two asymptotic scenarios, both of which are identical in a Euclidean geometry. For both scenarios, s... |
Title: Tableaux for the Lambek-Grishin calculus |
Abstract: Categorial type logics, pioneered by Lambek, seek a proof-theoretic understanding of natural language syntax by identifying categories with formulas and derivations with proofs. We typically observe an intuitionistic bias: a structural configuration of hypotheses (a constituent) derives a single conclusion (t... |
Title: A Unified View of Regularized Dual Averaging and Mirror Descent with Implicit Updates |
Abstract: We study three families of online convex optimization algorithms: follow-the-proximally-regularized-leader (FTRL-Proximal), regularized dual averaging (RDA), and composite-objective mirror descent. We first prove equivalence theorems that show all of these algorithms are instantiations of a general FTRL updat... |
Title: Niche as a determinant of word fate in online groups |
Abstract: Patterns of word use both reflect and influence a myriad of human activities and interactions. Like other entities that are reproduced and evolve, words rise or decline depending upon a complex interplay between their intrinsic properties and the environments in which they function. Using Internet discussion ... |
Title: Conditional Random Fields and Support Vector Machines: A Hybrid Approach |
Abstract: We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs). We provide a sufficient condition for when the hybrid loss is Fisher consistent for cla... |
Title: Hierarchical Modeling of Abundance in Closed Population Capture-Recapture Models Under Heterogeneity |
Abstract: Hierarchical modeling of abundance in space or time using closed-population mark-recapture under heterogeneity (model M$_h$) presents two challenges: (i) finding a flexible likelihood in which abundance appears as an explicit parameter and (ii) fitting the hierarchical model for abundance. The first challenge... |
Title: Safe Feature Elimination in Sparse Supervised Learning |
Abstract: We investigate fast methods that allow to quickly eliminate variables (features) in supervised learning problems involving a convex loss function and a $l_1$-norm penalty, leading to a potentially substantial reduction in the number of variables prior to running the supervised learning algorithm. The methods ... |
Title: Social Spiral Pattern in Experimental 2x2 Games |
Abstract: With evolutionary game theory, mathematicians, physicists and theoretical biologists usually show us beautiful figures of population dynamic patterns. 2x2 game (matching pennies game) is one of the classical cases. In this letter, we report our finding that, there exists a dynamical pattern, called as social ... |
Title: Mobile Testbeds with an Attitude |
Abstract: There have been significant recent advances in mobile networks, specifically in multi-hop wireless networks including DTNs and sensor networks. It is critical to have a testing environment to realistically evaluate such networks and their protocols and services. Towards this goal, we propose a novel, mobile t... |
Title: Deep Self-Taught Learning for Handwritten Character Recognition |
Abstract: Recent theoretical and empirical work in statistical machine learning has demonstrated the importance of learning algorithms for deep architectures, i.e., function classes obtained by composing multiple non-linear transformations. Self-taught learning (exploiting unlabeled examples or examples from other dist... |
Title: Pair-Wise Cluster Analysis |
Abstract: This paper studies the problem of learning clusters which are consistently present in different (continuously valued) representations of observed data. Our setup differs slightly from the standard approach of (co-) clustering as we use the fact that some form of `labeling' becomes available in this setup: a c... |
Title: Geometric Decision Tree |
Abstract: In this paper we present a new algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess the goodness of hyperplanes at each node while learning a decision tree in a top-down fashion. These impurity measures do not properly capture the geom... |
Title: On the Doubt about Margin Explanation of Boosting |
Abstract: Margin theory provides one of the most popular explanations to the success of , where the central point lies in the recognition that is the key for characterizing the performance of . This theory has been very influential, e.g., it has been used to argue that usually does not overfit since it tends to enlarge... |
Title: Robust graphical modeling of gene networks using classical and alternative T-distributions |
Abstract: Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data. Applications to studies of gene expression have generated substantial interest in these models, and resulting recent progress includes the development of fitting methodology involving penaliza... |
Title: Totally Corrective Multiclass Boosting with Binary Weak Learners |
Abstract: In this work, we propose a new optimization framework for multiclass boosting learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two successful multiclass boosting algorithms, which can use binary weak learners. We explicitly derive these two algorithms' Lagrange dual problems based on their re... |
Title: Structural Learning of Attack Vectors for Generating Mutated XSS Attacks |
Abstract: Web applications suffer from cross-site scripting (XSS) attacks that resulting from incomplete or incorrect input sanitization. Learning the structure of attack vectors could enrich the variety of manifestations in generated XSS attacks. In this study, we focus on generating more threatening XSS attacks for t... |
Title: Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees |
Abstract: Recently there is a line of research work proposing to employ Spectral Clustering (SC) to segment (group)Throughout the paper, we use segmentation, clustering, and grouping, and their verb forms, interchangeably. high-dimensional structural data such as those (approximately) lying on subspaces We follow liu20... |
Title: Fast Sparse Decomposition by Iterative Detection-Estimation |
Abstract: Finding sparse solutions of underdetermined systems of linear equations is a fundamental problem in signal processing and statistics which has become a subject of interest in recent years. In general, these systems have infinitely many solutions. However, it may be shown that sufficiently sparse solutions may... |
Title: Optimistic Rates for Learning with a Smooth Loss |
Abstract: We establish an excess risk bound of O(H R_n^2 + R_n ) for empirical risk minimization with an H-smooth loss function and a hypothesis class with Rademacher complexity R_n, where L* is the best risk achievable by the hypothesis class. For typical hypothesis classes where R_n = , this translates to a learning ... |
Title: Approximate Inference and Stochastic Optimal Control |
Abstract: We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic optimal contro... |
Title: A family of statistical symmetric divergences based on Jensen's inequality |
Abstract: We introduce a novel parametric family of symmetric information-theoretic distances based on Jensen's inequality for a convex functional generator. In particular, this family unifies the celebrated Jeffreys divergence with the Jensen-Shannon divergence when the Shannon entropy generator is chosen. We then des... |
Title: Safe Feature Elimination for the LASSO and Sparse Supervised Learning Problems |
Abstract: We describe a fast method to eliminate features (variables) in l1 -penalized least-square regression (or LASSO) problems. The elimination of features leads to a potentially substantial reduction in running time, specially for large values of the penalty parameter. Our method is not heuristic: it only eliminat... |
Title: Gaussian process single-index models as emulators for computer experiments |
Abstract: A single-index model (SIM) provides for parsimonious multi-dimensional nonlinear regression by combining parametric (linear) projection with univariate nonparametric (non-linear) regression models. We show that a particular Gaussian process (GP) formulation is simple to work with and ideal as an emulator for ... |
Title: Identification of discrete concentration graph models with one hidden binary variable |
Abstract: Conditions are presented for different types of identifiability of discrete variable models generated over an undirected graph in which one node represents a binary hidden variable. These models can be seen as extensions of the latent class model to allow for conditional associations between the observable ra... |
Title: A new closed-loop output error method for parameter identification of robot dynamics |
Abstract: Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techn... |
Title: A hybrid learning algorithm for text classification |
Abstract: Text classification is the process of classifying documents into predefined categories based on their content. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification that requires fewer d... |
Title: 3D-Mesh denoising using an improved vertex based anisotropic diffusion |
Abstract: This paper deals with an improvement of vertex based nonlinear diffusion for mesh denoising. This method directly filters the position of the vertices using Laplace, reduced centered Gaussian and Rayleigh probability density functions as diffusivities. The use of these PDFs improves the performance of a verte... |
Title: Text Classification using the Concept of Association Rule of Data Mining |
Abstract: As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic classification of text can provide this information at low cost, b... |
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