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Title: A Monte Carlo AIXI Approximation |
Abstract: This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a Bayesian optimality notion for general reinforcement learning agents. Previously, it has been unclear whether the theory of AIXI could motiv... |
Title: High-Dimensional Non-Linear Variable Selection through Hierarchical Kernel Learning |
Abstract: We consider the problem of high-dimensional non-linear variable selection for supervised learning. Our approach is based on performing linear selection among exponentially many appropriately defined positive definite kernels that characterize non-linear interactions between the original variables. To select e... |
Title: Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets |
Abstract: Scaling of proposals for Metropolis algorithms is an important practical problem in MCMC implementation. Criteria for scaling based on empirical acceptance rates of algorithms have been found to work consistently well across a broad range of problems. Essentially, proposal jump sizes are increased when accept... |
Title: Assessing the Impact of Informedness on a Consultant's Profit |
Abstract: We study the notion of informedness in a client-consultant setting. Using a software simulator, we examine the extent to which it pays off for consultants to provide their clients with advice that is well-informed, or with advice that is merely meant to appear to be well-informed. The latter strategy is benef... |
Title: Tuning parameter selection for penalized likelihood estimation of inverse covariance matrix |
Abstract: In a Gaussian graphical model, the conditional independence between two variables are characterized by the corresponding zero entries in the inverse covariance matrix. Maximum likelihood method using the smoothly clipped absolute deviation (SCAD) penalty (Fan and Li, 2001) and the adaptive LASSO penalty (Zou,... |
Title: Kernels for Measures Defined on the Gram Matrix of their Support |
Abstract: We present in this work a new family of kernels to compare positive measures on arbitrary spaces $\Xcal$ endowed with a positive kernel $\kappa$, which translates naturally into kernels between histograms or clouds of points. We first cover the case where $\Xcal$ is Euclidian, and focus on kernels which take ... |
Title: Rejoinder: Harold Jeffreys's Theory of Probability Revisited |
Abstract: We are grateful to all discussants of our re-visitation for their strong support in our enterprise and for their overall agreement with our perspective. Further discussions with them and other leading statisticians showed that the legacy of Theory of Probability is alive and lasting. [arXiv:0804.3173] |
Title: A multiagent urban traffic simulation Part I: dealing with the ordinary |
Abstract: We describe in this article a multiagent urban traffic simulation, as we believe individual-based modeling is necessary to encompass the complex influence the actions of an individual vehicle can have on the overall flow of vehicles. We first describe how we build a graph description of the network from purel... |
Title: Asymptotic near-efficiency of the ''Gibbs-energy (GE) and empirical-variance'' estimating functions for fitting Mat\'ern models -- II: Accounting for measurement errors via ''conditional GE mean'' |
Abstract: Consider one realization of a continuous-time Gaussian process $Z$ which belongs to the Mat\' ern family with known ``regularity'' index $\nu >0$. For estimating the autocorrelation-range and the variance of $Z$ from $n$ observations on a fine grid, we studied in Girard (2016) the GE-EV method which simply re... |
Title: New Approximation Algorithms for Minimum Enclosing Convex Shapes |
Abstract: Given $n$ points in a $d$ dimensional Euclidean space, the Minimum Enclosing Ball (MEB) problem is to find the ball with the smallest radius which contains all $n$ points. We give a $O(nd\Qcal/)$ approximation algorithm for producing an enclosing ball whose radius is at most $\epsilon$ away from the optimum (... |
Title: Shrinkage Tuning Parameter Selection in Precision Matrices Estimation |
Abstract: Recent literature provides many computational and modeling approaches for covariance matrices estimation in a penalized Gaussian graphical models but relatively little study has been carried out on the choice of the tuning parameter. This paper tries to fill this gap by focusing on the problem of shrinkage pa... |
Title: Empowering OLAC Extension using Anusaaraka and Effective text processing using Double Byte coding |
Abstract: The paper reviews the hurdles while trying to implement the OLAC extension for Dravidian / Indian languages. The paper further explores the possibilities which could minimise or solve these problems. In this context, the Chinese system of text processing and the anusaaraka system are scrutinised. |
Title: n-Opposition theory to structure debates |
Abstract: 2007 was the first international congress on the ?square of oppositions?. A first attempt to structure debate using n-opposition theory was presented along with the results of a first experiment on the web. Our proposal for this paper is to define relations between arguments through a structure of opposition ... |
Title: Scheme of thinking quantum systems |
Abstract: A general approach describing quantum decision procedures is developed. The approach can be applied to quantum information processing, quantum computing, creation of artificial quantum intelligence, as well as to analyzing decision processes of human decision makers. Our basic point is to consider an active q... |
Title: High-dimensional Graphical Model Search with gRapHD R Package |
Abstract: This paper presents the R package gRapHD for efficient selection of high-dimensional undirected graphical models. The package provides tools for selecting trees, forests and decomposable models minimizing information criteria such as AIC or BIC, and for displaying the independence graphs of the models. It has... |
Title: Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling |
Abstract: Conditional Random Fields (CRFs) constitute a popular and efficient approach for supervised sequence labelling. CRFs can cope with large description spaces and can integrate some form of structural dependency between labels. In this contribution, we address the issue of efficient feature selection for CRFs ba... |
Title: Sparse image representation by discrete cosine/spline based dictionaries |
Abstract: Mixed dictionaries generated by cosine and B-spline functions are considered. It is shown that, by highly nonlinear approaches such as Orthogonal Matching Pursuit, the discrete version of the proposed dictionaries yields a significant gain in the sparsity of an image representation. |
Title: Lower Bounds for BMRM and Faster Rates for Training SVMs |
Abstract: Regularized risk minimization with the binary hinge loss and its variants lies at the heart of many machine learning problems. Bundle methods for regularized risk minimization (BMRM) and the closely related SVMStruct are considered the best general purpose solvers to tackle this problem. It was recently shown... |
Title: Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping |
Abstract: We consider the problem of estimating a sparse multi-response regression function, with an application to expression quantitative trait locus (eQTL) mapping, where the goal is to discover genetic variations that influence gene-expression levels. In particular, we investigate a shrinkage technique capable of c... |
Title: Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment |
Abstract: Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environme... |
Title: On Ranking Senators By Their Votes |
Abstract: The problem of ranking a set of objects given some measure of similarity is one of the most basic in machine learning. Recently Agarwal proposed a method based on techniques in semi-supervised learning utilizing the graph Laplacian. In this work we consider a novel application of this technique to ranking bin... |
Title: Structured Sparse Principal Component Analysis |
Abstract: We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This is based on a structured regularization recently introduced by [1]. While classical sparse priors only dea... |
Title: Accuracy Improvement for Stiffness Modeling of Parallel Manipulators |
Abstract: The paper focuses on the accuracy improvement of stiffness models for parallel manipulators, which are employed in high-speed precision machining. It is based on the integrated methodology that combines analytical and numerical techniques and deals with multidimensional lumped-parameter models of the links. T... |
Title: Design optimization of parallel manipulators for high-speed precision machining applications |
Abstract: The paper proposes an integrated approach to the design optimization of parallel manipulators, which is based on the concept of the workspace grid and utilizes the goal-attainment formulation for the global optimization. To combine the non-homogenous design specification, the developed optimization technique ... |
Title: Estimating migration proportions from discretely observed continuous diffusion processes |
Abstract: We model two time and space scales discrete observations by using a unique continuous diffusion process with time dependent coefficient. We define new parameters for the large scale model as functions of the small scale distribution cumulants. We use the non - uniform distribution of the observation time inte... |
Title: Kernel Spectral Curvature Clustering (KSCC) |
Abstract: Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for modeling data by mixtures of affine subspaces (which is often r... |
Title: Motion Segmentation by SCC on the Hopkins 155 Database |
Abstract: We apply the Spectral Curvature Clustering (SCC) algorithm to a benchmark database of 155 motion sequences, and show that it outperforms all other state-of-the-art methods. The average misclassification rate by SCC is 1.41% for sequences having two motions and 4.85% for three motions. |
Title: Structure Variability in Bayesian Networks |
Abstract: The structure of a Bayesian network encodes most of the information about the probability distribution of the data, which is uniquely identified given some general distributional assumptions. Therefore it's important to study the variability of its network structure, which can be used to compare the performan... |
Title: Interactive Data Integration through Smart Copy & Paste |
Abstract: In many scenarios, such as emergency response or ad hoc collaboration, it is critical to reduce the overhead in integrating data. Ideally, one could perform the entire process interactively under one unified interface: defining extractors and wrappers for sources, creating a mediated schema, and adding schema... |
Title: Sailing the Information Ocean with Awareness of Currents: Discovery and Application of Source Dependence |
Abstract: The Web has enabled the availability of a huge amount of useful information, but has also eased the ability to spread false information and rumors across multiple sources, making it hard to distinguish between what is true and what is not. Recent examples include the premature Steve Jobs obituary, the second ... |
Title: Greedy Gossip with Eavesdropping |
Abstract: This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and... |
Title: Data-driven calibration of linear estimators with minimal penalties |
Abstract: This paper tackles the problem of selecting among several linear estimators in non-parametric regression; this includes model selection for linear regression, the choice of a regularization parameter in kernel ridge regression, spline smoothing or locally weighted regression, and the choice of a kernel in mul... |
Title: Non-asymptotic model selection for linear non least-squares estimation in regression models and inverse problems |
Abstract: We propose to address the common problem of linear estimation in linear statistical models by using a model selection approach via penalization. Depending then on the framework in which the linear statistical model is considered namely the regression framework or the inverse problem framework, a data-driven m... |
Title: Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary $\beta$-Mixing Processes |
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