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Title: An Adaptive Sequential Monte Carlo Sampler |
Abstract: Sequential Monte Carlo (SMC) methods are not only a popular tool in the analysis of state space models, but offer an alternative to MCMC in situations where Bayesian inference must proceed via simulation. This paper introduces a new SMC method that uses adaptive MCMC kernels for particle dynamics. The propose... |
Title: Parametric inference in a perturbed gamma degradation process |
Abstract: We consider the gamma process perturbed by a Brownian motion (independent of the gamma process) as a degradation model. Parameters estimation is studied here. We assume that $n$ independent items are observed at irregular instants. From these observations, we estimate the parameters using the moments method. ... |
Title: Efficient computation of the cdf of the maximal difference between Brownian bridge and its concave majorant |
Abstract: In this paper, we describe two computational methods for calculating the cumulative distribution function and the upper quantiles of the maximal difference between a Brownian bridge and its concave majorant. The first method has two different variants that are both based on a Monte Carlo approach, whereas the... |
Title: Improving the Johnson-Lindenstrauss Lemma |
Abstract: The Johnson-Lindenstrauss Lemma allows for the projection of $n$ points in $p-$dimensional Euclidean space onto a $k-$dimensional Euclidean space, with $k \ge 3\epsilon^2-2\epsilon^3$, so that the pairwise distances are preserved within a factor of $1\pm\epsilon$. Here, working directly with the distributions... |
Title: Classification via Incoherent Subspaces |
Abstract: This article presents a new classification framework that can extract individual features per class. The scheme is based on a model of incoherent subspaces, each one associated to one class, and a model on how the elements in a class are represented in this subspace. After the theoretical analysis an alternat... |
Title: How to correctly prune tropical trees |
Abstract: We present tropical games, a generalization of combinatorial min-max games based on tropical algebras. Our model breaks the traditional symmetry of rational zero-sum games where players have exactly opposed goals (min vs. max), is more widely applicable than min-max and also supports a form of pruning, despit... |
Title: Yet another breakdown point notion: EFSBP - illustrated at scale-shape models |
Abstract: The breakdown point in its different variants is one of the central notions to quantify the global robustness of a procedure. We propose a simple supplementary variant which is useful in situations where we have no obvious or only partial equivariance: Extending the Donoho and Huber(1983) Finite Sample Breakd... |
Title: Recognizability of Individual Creative Style Within and Across Domains: Preliminary Studies |
Abstract: It is hypothesized that creativity arises from the self-mending capacity of an internal model of the world, or worldview. The uniquely honed worldview of a creative individual results in a distinctive style that is recognizable within and across domains. It is further hypothesized that creativity is domaingen... |
Title: Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection |
Abstract: Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been found helpful in many situations, they may degenerate performance and the resultant generalization ability may be even worse than usin... |
Title: On The Power of Tree Projections: Structural Tractability of Enumerating CSP Solutions |
Abstract: The problem of deciding whether CSP instances admit solutions has been deeply studied in the literature, and several structural tractability results have been derived so far. However, constraint satisfaction comes in practice as a computation problem where the focus is either on finding one solution, or on en... |
Title: Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines |
Abstract: We improve recently published results about resources of Restricted Boltzmann Machines (RBM) and Deep Belief Networks (DBN) required to make them Universal Approximators. We show that any distribution p on the set of binary vectors of length n can be arbitrarily well approximated by an RBM with k-1 hidden uni... |
Title: On Macroscopic Complexity and Perceptual Coding |
Abstract: The theoretical limits of 'lossy' data compression algorithms are considered. The complexity of an object as seen by a macroscopic observer is the size of the perceptual code which discards all information that can be lost without altering the perception of the specified observer. The complexity of this macro... |
Title: Heuristics in Conflict Resolution |
Abstract: Modern solvers for Boolean Satisfiability (SAT) and Answer Set Programming (ASP) are based on sophisticated Boolean constraint solving techniques. In both areas, conflict-driven learning and related techniques constitute key features whose application is enabled by conflict analysis. Although various conflict... |
Title: Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes |
Abstract: Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions reliably. Large and rich sets of features can cause existing algorithms to overfit because of a limited number of samples. We address... |
Title: On the estimation of integrated covariance matrices of high dimensional diffusion processes |
Abstract: We consider the estimation of integrated covariance (ICV) matrices of high dimensional diffusion processes based on high frequency observations. We start by studying the most commonly used estimator, the realized covariance (RCV) matrix. We show that in the high dimensional case when the dimension $p$ and the... |
Title: Prediction with Expert Advice under Discounted Loss |
Abstract: We study prediction with expert advice in the setting where the losses are accumulated with some discounting---the impact of old losses may gradually vanish. We generalize the Aggregating Algorithm and the Aggregating Algorithm for Regression to this case, propose a suitable new variant of exponential weights... |
Title: Scalable Probabilistic Databases with Factor Graphs and MCMC |
Abstract: Probabilistic databases play a crucial role in the management and understanding of uncertain data. However, incorporating probabilities into the semantics of incomplete databases has posed many challenges, forcing systems to sacrifice modeling power, scalability, or restrict the class of relational algebra fo... |
Title: The solution path of the generalized lasso |
Abstract: We present a path algorithm for the generalized lasso problem. This problem penalizes the $\ell_1$ norm of a matrix D times the coefficient vector, and has a wide range of applications, dictated by the choice of D. Our algorithm is based on solving the dual of the generalized lasso, which greatly facilitates ... |
Title: Robustness of Optimal Designs for 2^2 Experiments with Binary Response |
Abstract: We consider an experiment with two qualitative factors at 2 levels each and a binary response, that follows a generalized linear model. In Mandal, Yang and Majumdar (2010) we obtained basic results and characterizations of locally D-optimal designs for special cases. As locally optimal designs depend on the a... |
Title: Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling |
Abstract: The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly nonsmooth) convex functions using only local computation and communication. It arises in various application domains, including distributed tracking and localization, multi-agent co-ordin... |
Title: A self-normalized approach to confidence interval construction in time series |
Abstract: We propose a new method to construct confidence intervals for quantities that are associated with a stationary time series, which avoids direct estimation of the asymptotic variances. Unlike the existing tuning-parameter-dependent approaches, our method has the attractive convenience of being free of choosing... |
Title: On the Finite Time Convergence of Cyclic Coordinate Descent Methods |
Abstract: Cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning. Reasons for this include its simplicity, speed and stability, as well as its competitive performance on $\ell_1$ regularized smooth optimization problems. Surprisingly, very little is kn... |
Title: Detecting Blackholes and Volcanoes in Directed Networks |
Abstract: In this paper, we formulate a novel problem for finding blackhole and volcano patterns in a large directed graph. Specifically, a blackhole pattern is a group which is made of a set of nodes in a way such that there are only inlinks to this group from the rest nodes in the graph. In contrast, a volcano patter... |
Title: A general method for debiasing a Monte Carlo estimator |
Abstract: Consider a process, stochastic or deterministic, obtained by using a numerical integration scheme, or from Monte-Carlo methods involving an approximation to an integral, or a Newton-Raphson iteration to approximate the root of an equation. We will assume that we can sample from the distribution of the process... |
Title: Ecological non-linear state space model selection via adaptive particle Markov chain Monte Carlo (AdPMCMC) |
Abstract: We develop a novel advanced Particle Markov chain Monte Carlo algorithm that is capable of sampling from the posterior distribution of non-linear state space models for both the unobserved latent states and the unknown model parameters. We apply this novel methodology to five population growth models, includi... |
Title: Robustness and Generalization |
Abstract: We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from the complexity or stability arguments, to study gene... |
Title: Context models on sequences of covers |
Abstract: We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and maintaining a different model for each set within a cov... |
Title: Online Learning of Noisy Data with Kernels |
Abstract: We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with no restriction other than having zero mean and bounded variance. Our technique relies on a family of unbiased estimators for non-lin... |
Title: Towards Physarum Binary Adders |
Abstract: Plasmodium of is a single cell visible by unaided eye. The plasmodium's foraging behaviour is interpreted in terms of computation. Input data is a configuration of nutrients, result of computation is a network of plasmodium's cytoplasmic tubes spanning sources of nutrients. Tsuda et al (2004) experimentally d... |
Title: Some comments on C. S. Wallace's random number generators |
Abstract: We outline some of Chris Wallace's contributions to pseudo-random number generation. In particular, we consider his idea for generating normally distributed variates without relying on a source of uniform random numbers, and compare it with more conventional methods for generating normal random numbers. Imple... |
Title: On a Multiplicative Algorithm for Computing Bayesian D-optimal Designs |
Abstract: We use the minorization-maximization principle (Lange, Hunter and Yang 2000) to establish the monotonicity of a multiplicative algorithm for computing Bayesian D-optimal designs. This proves a conjecture of Dette, Pepelyshev and Zhigljavsky (2008). |
Title: A Short Introduction to Model Selection, Kolmogorov Complexity and Minimum Description Length (MDL) |
Abstract: The concept of overfitting in model selection is explained and demonstrated with an example. After providing some background information on information theory and Kolmogorov complexity, we provide a short explanation of Minimum Description Length and error minimization. We conclude with a discussion of the ty... |
Title: Eigenvectors for clustering: Unipartite, bipartite, and directed graph cases |
Abstract: This paper presents a concise tutorial on spectral clustering for broad spectrum graphs which include unipartite (undirected) graph, bipartite graph, and directed graph. We show how to transform bipartite graph and directed graph into corresponding unipartite graph, therefore allowing a unified treatment to a... |
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