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Title: Minimum mean square distance estimation of a subspace |
Abstract: We consider the problem of subspace estimation in a Bayesian setting. Since we are operating in the Grassmann manifold, the usual approach which consists of minimizing the mean square error (MSE) between the true subspace $U$ and its estimate $$ may not be adequate as the MSE is not the natural metric in the ... |
Title: The "psychological map of the brain", as a personal information card (file), - a project for the student of the 21st century |
Abstract: We suggest a procedure that is relevant both to electronic performance and human psychology, so that the creative logic and the respect for human nature appear in a good agreement. The idea is to create an electronic card containing basic information about a person's psychological behavior in order to make it... |
Title: Defining a robust biological prior from Pathway Analysis to drive Network Inference |
Abstract: Inferring genetic networks from gene expression data is one of the most challenging work in the post-genomic era, partly due to the vast space of possible networks and the relatively small amount of data available. In this field, Gaussian Graphical Model (GGM) provides a convenient framework for the discovery... |
Title: Convergence rates of efficient global optimization algorithms |
Abstract: Efficient global optimization is the problem of minimizing an unknown function f, using as few evaluations f(x) as possible. It can be considered as a continuum-armed bandit problem, with noiseless data and simple regret. Expected improvement is perhaps the most popular method for solving this problem; the al... |
Title: Infinity in computable probability |
Abstract: Does combining a finite collection of objects infinitely many times guarantee the construction of a particular object? Here we use recursive function theory to examine the popular scenario of an infinite collection of typing monkeys reproducing the works of Shakespeare. Our main result is to show that it is p... |
Title: Classification under Data Contamination with Application to Remote Sensing Image Mis-registration |
Abstract: This work is motivated by the problem of image mis-registration in remote sensing and we are interested in determining the resulting loss in the accuracy of pattern classification. A statistical formulation is given where we propose to use data contamination to model and understand the phenomenon of image mis... |
Title: Generic identification of binary-valued hidden Markov processes |
Abstract: The generic identification problem is to decide whether a stochastic process $(X_t)$ is a hidden Markov process and if yes to infer its parameters for all but a subset of parametrizations that form a lower-dimensional subvariety in parameter space. Partial answers so far available depend on extra assumptions ... |
Title: Transductive-Inductive Cluster Approximation Via Multivariate Chebyshev Inequality |
Abstract: Approximating adequate number of clusters in multidimensional data is an open area of research, given a level of compromise made on the quality of acceptable results. The manuscript addresses the issue by formulating a transductive inductive learning algorithm which uses multivariate Chebyshev inequality. Con... |
Title: Dyna-H: a heuristic planning reinforcement learning algorithm applied to role-playing-game strategy decision systems |
Abstract: In a Role-Playing Game, finding optimal trajectories is one of the most important tasks. In fact, the strategy decision system becomes a key component of a game engine. Determining the way in which decisions are taken (online, batch or simulated) and the consumed resources in decision making (e.g. execution t... |
Title: Context Capture in Software Development |
Abstract: The context of a software developer is something hard to define and capture, as it represents a complex network of elements across different dimensions that are not limited to the work developed on an IDE. We propose the definition of a software developer context model that takes into account all the dimensio... |
Title: Evolutionary Mechanics: new engineering principles for the emergence of flexibility in a dynamic and uncertain world |
Abstract: Engineered systems are designed to deftly operate under predetermined conditions yet are notoriously fragile when unexpected perturbations arise. In contrast, biological systems operate in a highly flexible manner; learn quickly adequate responses to novel conditions, and evolve new routines/traits to remain ... |
Title: The Role of Normalization in the Belief Propagation Algorithm |
Abstract: An important part of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov Random Field. The belief propagation algorithm, which is an exact procedure to compute these marginals when the underlying graph is a tree, has gained its popul... |
Title: A fast and recursive algorithm for clustering large datasets with $k$-medians |
Abstract: Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. Borrowing ideas from MacQueen (1967) who introduced a sequential version of the $k$-means algorithm, a new class of recursive stochastic gradient algorithms designed for the $k$-median... |
Title: A Novel Approach for Fast Detection of Multiple Change Points in Linear Models |
Abstract: A change point problem occurs in many statistical applications. If there exist change points in a model, it is harmful to make a statistical analysis without any consideration of the existence of the change points and the results derived from such an analysis may be misleading. There are rich literatures on c... |
Title: Statistical Mechanics of Semi-Supervised Clustering in Sparse Graphs |
Abstract: We theoretically study semi-supervised clustering in sparse graphs in the presence of pairwise constraints on the cluster assignments of nodes. We focus on bi-cluster graphs, and study the impact of semi-supervision for varying constraint density and overlap between the clusters. Recent results for unsupervis... |
Title: Efficient Bayesian inference in stochastic chemical kinetic models using graphical processing units |
Abstract: A goal of systems biology is to understand the dynamics of intracellular systems. Stochastic chemical kinetic models are often utilized to accurately capture the stochastic nature of these systems due to low numbers of molecules. Collecting system data allows for estimation of stochastic chemical kinetic rate... |
Title: Diffusion framework for geometric and photometric data fusion in non-rigid shape analysis |
Abstract: In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embeddi... |
Title: A Partitioning Deletion/Substitution/Addition Algorithm for Creating Survival Risk Groups |
Abstract: Accurately assessing a patient's risk of a given event is essential in making informed treatment decisions. One approach is to stratify patients into two or more distinct risk groups with respect to a specific outcome using both clinical and demographic variables. Outcomes may be categorical or continuous in ... |
Title: Building a Chaotic Proved Neural Network |
Abstract: Chaotic neural networks have received a great deal of attention these last years. In this paper we establish a precise correspondence between the so-called chaotic iterations and a particular class of artificial neural networks: global recurrent multi-layer perceptrons. We show formally that it is possible to... |
Title: Meaning Negotiation as Inference |
Abstract: Meaning negotiation (MN) is the general process with which agents reach an agreement about the meaning of a set of terms. Artificial Intelligence scholars have dealt with the problem of MN by means of argumentations schemes, beliefs merging and information fusion operators, and ontology alignment but the prop... |
Title: Inferences in Bayesian variable selection problems with large model spaces |
Abstract: An important aspect of Bayesian model selection is how to deal with huge model spaces, since exhaustive enumeration of all the models entertained is unfeasible and inferences have to be based on the very small proportion of models visited. This is the case for the variable selection problem, with a moderate t... |
Title: Statistical Multiresolution Dantzig Estimation in Imaging: Fundamental Concepts and Algorithmic Framework |
Abstract: In this paper we are concerned with fully automatic and locally adaptive estimation of functions in a "signal + noise"-model where the regression function may additionally be blurred by a linear operator, e.g. by a convolution. To this end, we introduce a general class of statistical multiresolution estimator... |
Title: Reproducing Kernel Banach Spaces with the l1 Norm |
Abstract: Targeting at sparse learning, we construct Banach spaces B of functions on an input space X with the properties that (1) B possesses an l1 norm in the sense that it is isometrically isomorphic to the Banach space of integrable functions on X with respect to the counting measure; (2) point evaluations are cont... |
Title: Reproducing Kernel Banach Spaces with the l1 Norm II: Error Analysis for Regularized Least Square Regression |
Abstract: A typical approach in estimating the learning rate of a regularized learning scheme is to bound the approximation error by the sum of the sampling error, the hypothesis error and the regularization error. Using a reproducing kernel space that satisfies the linear representer theorem brings the advantage of di... |
Title: Adaptive Submodular Optimization under Matroid Constraints |
Abstract: Many important problems in discrete optimization require maximization of a monotonic submodular function subject to matroid constraints. For these problems, a simple greedy algorithm is guaranteed to obtain near-optimal solutions. In this article, we extend this classic result to a general class of adaptive o... |
Title: A Context-theoretic Framework for Compositionality in Distributional Semantics |
Abstract: Techniques in which words are represented as vectors have proved useful in many applications in computational linguistics, however there is currently no general semantic formalism for representing meaning in terms of vectors. We present a framework for natural language semantics in which words, phrases and se... |
Title: Finding undetected protein associations in cell signaling by belief propagation |
Abstract: External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partne... |
Title: Bayesian Variable Selection for Probit Mixed Models Applied to Gene Selection |
Abstract: In computational biology, gene expression datasets are characterized by very few individual samples compared to a large number of measurements per sample. Thus, it is appealing to merge these datasets in order to increase the number of observations and diversify the data, allowing a more reliable selection of... |
Title: Projective Limit Random Probabilities on Polish Spaces |
Abstract: A pivotal problem in Bayesian nonparametrics is the construction of prior distributions on the space M(V) of probability measures on a given domain V. In principle, such distributions on the infinite-dimensional space M(V) can be constructed from their finite-dimensional marginals---the most prominent example... |
Title: Close the Gaps: A Learning-while-Doing Algorithm for a Class of Single-Product Revenue Management Problems |
Abstract: We consider a retailer selling a single product with limited on-hand inventory over a finite selling season. Customer demand arrives according to a Poisson process, the rate of which is influenced by a single action taken by the retailer (such as price adjustment, sales commission, advertisement intensity, et... |
Title: Parallel Tempering with Equi-Energy Moves |
Abstract: The Equi-Energy Sampler (EES) introduced by Kou et al [2006] is based on a population of chains which are updated by local moves and global moves, also called equi-energy jumps. The state space is partitioned into energy rings, and the current state of a chain can jump to a past state of an adjacent chain tha... |
Title: Clustering functional data using wavelets |
Abstract: We present two methods for detecting patterns and clusters in high dimensional time-dependent functional data. Our methods are based on wavelet-based similarity measures, since wavelets are well suited for identifying highly discriminant local time and scale features. The multiresolution aspect of the wavelet... |
Title: Online Adaptive Decision Fusion Framework Based on Entropic Projections onto Convex Sets with Application to Wildfire Detection in Video |
Abstract: In this paper, an Entropy functional based online Adaptive Decision Fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms each of which yielding its own decision as a real number... |
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