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Title: Contextual Bandit Algorithms with Supervised Learning Guarantees |
Abstract: We address the problem of learning in an online, bandit setting where the learner must repeatedly select among $K$ actions, but only receives partial feedback based on its choices. We establish two new facts: First, using a new algorithm called Exp4.P, we show that it is possible to compete with the best in a... |
Title: The Degrees of Freedom of Partial Least Squares Regression |
Abstract: The derivation of statistical properties for Partial Least Squares regression can be a challenging task. The reason is that the construction of latent components from the predictor variables also depends on the response variable. While this typically leads to good performance and interpretable models in pract... |
Title: Design of a Smart Unmanned Ground Vehicle for Hazardous Environments |
Abstract: A smart Unmanned Ground Vehicle (UGV) is designed and developed for some application specific missions to operate predominantly in hazardous environments. In our work, we have developed a small and lightweight vehicle to operate in general cross-country terrains in or without daylight. The UGV can send visual... |
Title: Redundancy, Deduction Schemes, and Minimum-Size Bases for Association Rules |
Abstract: Association rules are among the most widely employed data analysis methods in the field of Data Mining. An association rule is a form of partial implication between two sets of binary variables. In the most common approach, association rules are parameterized by a lower bound on their confidence, which is the... |
Title: CLD-shaped Brushstrokes in Non-Photorealistic Rendering |
Abstract: Rendering techniques based on a random grid can be improved by adapting brushstrokes to the shape of different areas of the original picture. In this paper, the concept of Coherence Length Diagram is applied to determine the adaptive brushstrokes, in order to simulate an impressionist painting. Some examples ... |
Title: The distribution and quantiles of functionals of weighted empirical distributions when observations have different distributions |
Abstract: This paper extends Edgeworth-Cornish-Fisher expansions for the distribution and quantiles of nonparametric estimates in two ways. Firstly it allows observations to have different distributions. Secondly it allows the observations to be weighted in a predetermined way. The use of weighted estimates has a long ... |
Title: Nonparametric Estimation and On-Line Prediction for General Stationary Ergodic Sources |
Abstract: We proposed a learning algorithm for nonparametric estimation and on-line prediction for general stationary ergodic sources. We prepare histograms each of which estimates the probability as a finite distribution, and mixture them with weights to construct an estimator. The whole analysis is based on measure t... |
Title: Feature Importance in Bayesian Assessment of Newborn Brain Maturity from EEG |
Abstract: The methodology of Bayesian Model Averaging (BMA) is applied for assessment of newborn brain maturity from sleep EEG. In theory this methodology provides the most accurate assessments of uncertainty in decisions. However, the existing BMA techniques have been shown providing biased assessments in the absence ... |
Title: Principal Component Analysis with Contaminated Data: The High Dimensional Case |
Abstract: We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some (arbitrarily) corr... |
Title: Syntactic Topic Models |
Abstract: The syntactic topic model (STM) is a Bayesian nonparametric model of language that discovers latent distributions of words (topics) that are both semantically and syntactically coherent. The STM models dependency parsed corpora where sentences are grouped into documents. It assumes that each word is drawn fro... |
Title: Non-Central Limit Theorem Statistical Analysis for the "Long-tailed" Internet Society |
Abstract: This article presents a statistical analysis method and introduces the corresponding software package "tailstat," which is believed to be widely applicable to today's internet society. The proposed method facilitates statistical analyses with small sample sets from given populations, which render the central ... |
Title: A copula based approach to adaptive sampling |
Abstract: Our article is concerned with adaptive sampling schemes for Bayesian inference that update the proposal densities using previous iterates. We introduce a copula based proposal density which is made more efficient by combining it with antithetic variable sampling. We compare the copula based proposal to an ada... |
Title: Gaussian Process Structural Equation Models with Latent Variables |
Abstract: In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with latent variabl... |
Title: SLAM : Solutions lexicales automatique pour m\'etaphores |
Abstract: This article presents SLAM, an Automatic Solver for Lexical Metaphors like ?d\'eshabiller* une pomme? (to undress* an apple). SLAM calculates a conventional solution for these productions. To carry on it, SLAM has to intersect the paradigmatic axis of the metaphorical verb ?d\'eshabiller*?, where ?peler? (?to... |
Title: Less Regret via Online Conditioning |
Abstract: We analyze and evaluate an online gradient descent algorithm with adaptive per-coordinate adjustment of learning rates. Our algorithm can be thought of as an online version of batch gradient descent with a diagonal preconditioner. This approach leads to regret bounds that are stronger than those of standard o... |
Title: Adaptive Bound Optimization for Online Convex Optimization |
Abstract: We introduce a new online convex optimization algorithm that adaptively chooses its regularization function based on the loss functions observed so far. This is in contrast to previous algorithms that use a fixed regularization function such as L2-squared, and modify it only via a single time-dependent parame... |
Title: Asymptotic Analysis of Generative Semi-Supervised Learning |
Abstract: Semisupervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likelihood we quantify the asymptotic accuracy of generative semi-supervised learning. In doing so, we complement distribution-free analysis... |
Title: A New Understanding of Prediction Markets Via No-Regret Learning |
Abstract: We explore the striking mathematical connections that exist between market scoring rules, cost function based prediction markets, and no-regret learning. We show that any cost function based prediction market can be interpreted as an algorithm for the commonly studied problem of learning from expert advice by... |
Title: Comment on "Fastest learning in small-world neural networks" |
Abstract: This comment reexamines Simard et al.'s work in [D. Simard, L. Nadeau, H. Kroger, Phys. Lett. A 336 (2005) 8-15]. We found that Simard et al. calculated mistakenly the local connectivity lengths Dlocal of networks. The right results of Dlocal are presented and the supervised learning performance of feedforwar... |
Title: Security Analysis of Online Centroid Anomaly Detection |
Abstract: Security issues are crucial in a number of machine learning applications, especially in scenarios dealing with human activity rather than natural phenomena (e.g., information ranking, spam detection, malware detection, etc.). It is to be expected in such cases that learning algorithms will have to deal with m... |
Title: Non-Sparse Regularization for Multiple Kernel Learning |
Abstract: Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability. Unfortunately, this 1-norm MKL is rarely observed to out... |
Title: Learning from Logged Implicit Exploration Data |
Abstract: We provide a sound and consistent foundation for the use of exploration data in "contextual bandit" or "partially labeled" settings where only the value of a chosen action is learned. The primary challenge in a variety of settings is that the exploration policy, in which "offline" data is logged, is not expli... |
Title: A Contextual-Bandit Approach to Personalized News Article Recommendation |
Abstract: Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changi... |
Title: Product-limit estimators of the gap time distribution of a renewal process under different sampling patterns |
Abstract: Nonparametric estimation of the gap time distribution in a simple renewal process may be considered a problem in survival analysis under particular sampling frames corresponding to how the renewal process is observed. This note describes several such situations where simple product limit estimators, though in... |
Title: History of applications of martingales in survival analysis |
Abstract: The paper traces the development of the use of martingale methods in survival analysis from the mid 1970's to the early 1990's. This development was initiated by Aalen's Berkeley PhD-thesis in 1975, progressed through the work on estimation of Markov transition probabilities, non-parametric tests and Cox's re... |
Title: Detecting Weak but Hierarchically-Structured Patterns in Networks |
Abstract: The ability to detect weak distributed activation patterns in networks is critical to several applications, such as identifying the onset of anomalous activity or incipient congestion in the Internet, or faint traces of a biochemical spread by a sensor network. This is a challenging problem since weak distrib... |
Title: Why has (reasonably accurate) Automatic Speech Recognition been so hard to achieve? |
Abstract: Hidden Markov models (HMMs) have been successfully applied to automatic speech recognition for more than 35 years in spite of the fact that a key HMM assumption -- the statistical independence of frames -- is obviously violated by speech data. In fact, this data/model mismatch has inspired many attempts to mo... |
Title: Perfect simulation using dominated coupling from the past with application to area-interaction point processes and wavelet thresholding |
Abstract: We consider perfect simulation algorithms for locally stable point processes based on dominated coupling from the past, and apply these methods in two different contexts. A new version of the algorithm is developed which is feasible for processes which are neither purely attractive nor purely repulsive. Such ... |
Title: Statistical inference for time-changed L\'evy processes via composite characteristic function estimation |
Abstract: In this article, the problem of semi-parametric inference on the parameters of a multidimensional L\'evy process $L_t$ with independent components based on the low-frequency observations of the corresponding time-changed L\'evy process $L_(t)$, where $$ is a nonnegative, nondecreasing real-valued process inde... |
Title: Kernel methods and minimum contrast estimators for empirical deconvolution |
Abstract: We survey classical kernel methods for providing nonparametric solutions to problems involving measurement error. In particular we outline kernel-based methodology in this setting, and discuss its basic properties. Then we point to close connections that exist between kernel methods and much newer approaches ... |
Title: Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows |
Abstract: As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performances in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DXA, including the antigen... |
Title: Change of word types to word tokens ratio in the course of translation (based on Russian translations of K. Vonnegut novels) |
Abstract: The article provides lexical statistical analysis of K. Vonnegut's two novels and their Russian translations. It is found out that there happen some changes between the speed of word types and word tokens ratio change in the source and target texts. The author hypothesizes that these changes are typical for E... |
Title: libtissue - implementing innate immunity |
Abstract: In a previous paper the authors argued the case for incorporating ideas from innate immunity into articficial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were hughli... |
Title: Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition |
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