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Abstract: In large systems, it is important for agents to learn to act effectively, but sophisticated multi-agent learning algorithms generally do not scale. An alternative approach is to find restricted classes of games where simple, efficient algorithms converge. It is shown that stage learning efficiently converges ... |
Title: Differential Contrastive Divergence |
Abstract: This paper has been retracted. |
Title: Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling |
Abstract: We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional model selection in linear and Gaussian graphical models. Our conditions for consistency cover more general situations than those accomplished in previous work: we prove that restricted eigenvalue conditions (Bic... |
Title: Airport Gate Assignment A Hybrid Model and Implementation |
Abstract: With the rapid development of airlines, airports today become much busier and more complicated than previous days. During airlines daily operations, assigning the available gates to the arriving aircrafts based on the fixed schedule is a very important issue, which motivates researchers to study and solve Air... |
Title: Perfect simulation of spatial point processes using dominated coupling from the past with application to a multiscale area-interaction point process |
Abstract: We consider perfect simulation algorithms for locally stable point processes based on dominated coupling from the past. A version of the algorithm is developed which is feasible for processes which are neither purely attractive nor purely repulsive. Such processes include multiscale area-interaction processes... |
Title: Perfect simulation for Bayesian wavelet thresholding with correlated coefficients |
Abstract: We introduce a new method of Bayesian wavelet shrinkage for reconstructing a signal when we observe a noisy version. Rather than making the common assumption that the wavelet coefficients of the signal are independent, we allow for the possibility that they are locally correlated in both location (time) and s... |
Title: Dynamic Multi-Vehicle Routing with Multiple Classes of Demands |
Abstract: In this paper we study a dynamic vehicle routing problem in which there are multiple vehicles and multiple classes of demands. Demands of each class arrive in the environment randomly over time and require a random amount of on-site service that is characteristic of the class. To service a demand, one of the ... |
Title: Thermodynamics of Information Retrieval |
Abstract: In this work, we suggest a parameterized statistical model (the gamma distribution) for the frequency of word occurrences in long strings of English text and use this model to build a corresponding thermodynamic picture by constructing the partition function. We then use our partition function to compute ther... |
Title: A parameter-free hedging algorithm |
Abstract: We study the problem of decision-theoretic online learning (DTOL). Motivated by practical applications, we focus on DTOL when the number of actions is very large. Previous algorithms for learning in this framework have a tunable learning rate parameter, and a barrier to using online-learning in practical appl... |
Title: Tracking using explanation-based modeling |
Abstract: We study the tracking problem, namely, estimating the hidden state of an object over time, from unreliable and noisy measurements. The standard framework for the tracking problem is the generative framework, which is the basis of solutions such as the Bayesian algorithm and its approximation, the particle fil... |
Title: On $p$-adic Classification |
Abstract: A $p$-adic modification of the split-LBG classification method is presented in which first clusterings and then cluster centers are computed which locally minimise an energy function. The outcome for a fixed dataset is independent of the prime number $p$ with finitely many exceptions. The methods are applied ... |
Title: Analytic Bias Reduction for $k$-Sample Functionals |
Abstract: We give analytic methods for nonparametric bias reduction that remove the need for computationally intensive methods like the bootstrap and the jackknife. We call an estimate \it $p$th order if its bias has magnitude $n_0^-p$ as $n_0 \to \infty$, where $n_0$ is the sample size (or the minimum sample size if t... |
Title: Kalman Filtering with Intermittent Observations: Weak Convergence to a Stationary Distribution |
Abstract: The paper studies the asymptotic behavior of Random Algebraic Riccati Equations (RARE) arising in Kalman filtering when the arrival of the observations is described by a Bernoulli i.i.d. process. We model the RARE as an order-preserving, strongly sublinear random dynamical system (RDS). Under a sufficient con... |
Title: Optimistic Simulated Exploration as an Incentive for Real Exploration |
Abstract: Many reinforcement learning exploration techniques are overly optimistic and try to explore every state. Such exploration is impossible in environments with the unlimited number of states. I propose to use simulated exploration with an optimistic model to discover promising paths for real exploration. This re... |
Title: Learning with Structured Sparsity |
Abstract: This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea that has become po... |
Title: CDF and Survival Function Estimation with Infinite-Order Kernels |
Abstract: A reduced-bias nonparametric estimator of the cumulative distribution function (CDF) and the survival function is proposed using infinite-order kernels. Fourier transform theory on generalized functions is utilized to obtain the improved bias estimates. The new estimators are analyzed in terms of their relati... |
Title: Efficiently Learning a Detection Cascade with Sparse Eigenvectors |
Abstract: In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce Greedy Sparse Linear Discriminant Analysis (GSLDA) for its conceptual simplicity and computational efficiency; and slightly better detection pe... |
Title: Markov Random Field Segmentation of Brain MR Images |
Abstract: We describe a fully-automatic 3D-segmentation technique for brain MR images. Using Markov random fields the segmentation algorithm captures three important MR features, i.e. non-parametric distributions of tissue intensities, neighborhood correlations and signal inhomogeneities. Detailed simulations and real ... |
Title: Norm-Product Belief Propagation: Primal-Dual Message-Passing for Approximate Inference |
Abstract: In this paper we treat both forms of probabilistic inference, estimating marginal probabilities of the joint distribution and finding the most probable assignment, through a unified message-passing algorithm architecture. We generalize the Belief Propagation (BP) algorithms of sum-product and max-product and ... |
Title: Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model |
Abstract: This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the ob... |
Title: Nonstationarity-extended Whittle Estimation |
Abstract: For long memory time series models with uncorrelated but dependent errors, we establish the asymptotic normality of the Whittle estimator under mild conditions. Our framework includes the widely used FARIMA models with GARCH-type innovations. To cover nonstationary fractionally integrated processes, we extend... |
Title: A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data |
Abstract: Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world KDD applications. Existing outlier detection methods are ineffective on scattered real-world datasets due to implicit data patterns and parameter setting issues. We define a novel ... |
Title: Optimal Policies Search for Sensor Management |
Abstract: This paper introduces a new approach to solve sensor management problems. Classically sensor management problems can be well formalized as Partially-Observed Markov Decision Processes (POMPD). The original approach developped here consists in deriving the optimal parameterized policy based on a stochastic gra... |
Title: Distributed and Adaptive Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment |
Abstract: In this paper we present distributed and adaptive algorithms for motion coordination of a group of m autonomous vehicles. The vehicles operate in a convex environment with bounded velocity and must service demands whose time of arrival, location and on-site service are stochastic; the objective is to minimize... |
Title: How random are a learner's mistakes? |
Abstract: Given a random binary sequence $X^(n)$ of random variables, $X_t,$ $t=1,2,...,n$, for instance, one that is generated by a Markov source (teacher) of order $k^*$ (each state represented by $k^*$ bits). Assume that the probability of the event $X_t=1$ is constant and denote it by $\beta$. Consider a learner wh... |
Title: Comment on "Language Trees and Zipping" arXiv:cond-mat/0108530 |
Abstract: Every encoding has priori information if the encoding represents any semantic information of the unverse or object. Encoding means mapping from the unverse to the string or strings of digits. The semantic here is used in the model-theoretic sense or denotation of the object. If encoding or strings of symbols ... |
Title: Combinatorial Ricci Curvature and Laplacians for Image Processing |
Abstract: A new Combinatorial Ricci curvature and Laplacian operators for grayscale images are introduced and tested on 2D synthetic, natural and medical images. Analogue formulae for voxels are also obtained. These notions are based upon more general concepts developed by R. Forman. Further applications, in particular... |
Title: Designing a GUI for Proofs - Evaluation of an HCI Experiment |
Abstract: Often user interfaces of theorem proving systems focus on assisting particularly trained and skilled users, i.e., proof experts. As a result, the systems are difficult to use for non-expert users. This paper describes a paper and pencil HCI experiment, in which (non-expert) students were asked to make suggest... |
Title: Gradient-based adaptive interpolation in super-resolution image restoration |
Abstract: This paper presents a super-resolution method based on gradient-based adaptive interpolation. In this method, in addition to considering the distance between the interpolated pixel and the neighboring valid pixel, the interpolation coefficients take the local gradient of the original image into account. The s... |
Title: Switcher-random-walks: a cognitive-inspired mechanism for network exploration |
Abstract: Semantic memory is the subsystem of human memory that stores knowledge of concepts or meanings, as opposed to life specific experiences. The organization of concepts within semantic memory can be understood as a semantic network, where the concepts (nodes) are associated (linked) to others depending on percep... |
Title: Conditional Probability Tree Estimation Analysis and Algorithms |
Abstract: We consider the problem of estimating the conditional probability of a label in time $O(\log n)$, where $n$ is the number of possible labels. We analyze a natural reduction of this problem to a set of binary regression problems organized in a tree structure, proving a regret bound that scales with the depth o... |
Title: A Comparison of Analysis of Covariate-Adjusted Residuals and Analysis of Covariance |
Abstract: Various methods to control the influence of a covariate on a response variable are compared. In particular, ANOVA with or without homogeneity of variances (HOV) of errors and Kruskal-Wallis (K-W) tests on covariate-adjusted residuals and analysis of covariance (ANCOVA) are compared. Covariate-adjusted residua... |
Title: Expansions for Quantiles and Multivariate Moments of Extremes for Distributions of Pareto Type |
Abstract: Let $X_nr$ be the $r$th largest of a random sample of size $n$ from a distribution $F (x) = 1 - \sum_i = 0^\infty c_i x^-\alpha - i \beta$ for $\alpha > 0$ and $\beta > 0$. An inversion theorem is proved and used to derive an expansion for the quantile $F^-1 (u)$ and powers of it. From this an expansion in po... |
Title: Building the information kernel and the problem of recognition |
Abstract: At this point in time there is a need for a new representation of different information, to identify and organize descending its characteristics. Today, science is a powerful tool for the description of reality - the numbers. Why the most important property of numbers. Suppose we have a number 0.2351734, it i... |
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