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Title: Random DFAs are Efficiently PAC Learnable |
Abstract: This paper has been withdrawn due to an error found by Dana Angluin and Lev Reyzin. |
Title: Agent-Oriented Approach for Detecting and Managing Risks in Emergency Situations |
Abstract: This paper presents an agent-oriented approach to build a decision support system aimed at helping emergency managers to detect and to manage risks. We stress the flexibility and the adaptivity characteristics that are crucial to build a robust and efficient system, able to resolve complex problems. The syste... |
Title: Spontaneous organization leads to robustness in evolutionary algorithms |
Abstract: The interaction networks of biological systems are known to take on several non-random structural properties, some of which are believed to positively influence system robustness. Researchers are only starting to understand how these structural properties emerge, however suggested roles for component fitness ... |
Title: Some optimal criteria of model-robustness for two-level non-regular fractional factorial designs |
Abstract: We present some optimal criteria to evaluate model-robustness of non-regular two-level fractional factorial designs. Our method is based on minimizing the sum of squares of all the off-diagonal elements in the information matrix, and considering expectation under appropriate distribution functions for unknown... |
Title: Computational Scenario-based Capability Planning |
Abstract: Scenarios are pen-pictures of plausible futures, used for strategic planning. The aim of this investigation is to expand the horizon of scenario-based planning through computational models that are able to aid the analyst in the planning process. The investigation builds upon the advances of Information and C... |
Title: Generalized Collective Inference with Symmetric Clique Potentials |
Abstract: Collective graphical models exploit inter-instance associative dependence to output more accurate labelings. However existing models support very limited kind of associativity which restricts accuracy gains. This paper makes two major contributions. First, we propose a general collective inference framework t... |
Title: Credit Assignment in Adaptive Evolutionary Algorithms |
Abstract: In this paper, a new method for assigning credit to search operators is presented. Starting with the principle of optimizing search bias, search operators are selected based on an ability to create solutions that are historically linked to future generations. Using a novel framework for defining performance m... |
Title: Use of statistical outlier detection method in adaptive evolutionary algorithms |
Abstract: In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statisti... |
Title: Network Topology and Time Criticality Effects in the Modularised Fleet Mix Problem |
Abstract: In this paper, we explore the interplay between network topology and time criticality in a military logistics system. A general goal of this work (and previous work) is to evaluate land transportation requirements or, more specifically, how to design appropriate fleets of military general service vehicles tha... |
Title: Robustness and Adaptiveness Analysis of Future Fleets |
Abstract: Making decisions about the structure of a future military fleet is a challenging task. Several issues need to be considered such as the existence of multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the futur... |
Title: Open Problems in Universal Induction & Intelligence |
Abstract: Specialized intelligent systems can be found everywhere: finger print, handwriting, speech, and face recognition, spam filtering, chess and other game programs, robots, et al. This decade the first presumably complete mathematical theory of artificial intelligence based on universal induction-prediction-decis... |
Title: Bayesian Agglomerative Clustering with Coalescents |
Abstract: We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over others, and dem... |
Title: Bayesian Multitask Learning with Latent Hierarchies |
Abstract: We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks. We exploit the intuition that for domain adaptation, we wish to share classifier structure, but for multitask learning, we wish to share covariance structure. Our hierarchical model is seen to subsume severa... |
Title: Cross-Task Knowledge-Constrained Self Training |
Abstract: We present an algorithmic framework for learning multiple related tasks. Our framework exploits a form of prior knowledge that relates the output spaces of these tasks. We present PAC learning results that analyze the conditions under which such learning is possible. We present results on learning a shallow p... |
Title: A Bayesian Model for Discovering Typological Implications |
Abstract: A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as ``if objects come after verbs, then adjectives come after nouns.'' Such implications are typically discovered by painstaking hand analysis over a small ... |
Title: Search-based Structured Prediction |
Abstract: We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to wh... |
Title: Induction of Word and Phrase Alignments for Automatic Document Summarization |
Abstract: Current research in automatic single document summarization is dominated by two effective, yet naive approaches: summarization by sentence extraction, and headline generation via bag-of-words models. While successful in some tasks, neither of these models is able to adequately capture the large set of linguis... |
Title: A Noisy-Channel Model for Document Compression |
Abstract: We present a document compression system that uses a hierarchical noisy-channel model of text production. Our compression system first automatically derives the syntactic structure of each sentence and the overall discourse structure of the text given as input. The system then uses a statistical hierarchical ... |
Title: A Large-Scale Exploration of Effective Global Features for a Joint Entity Detection and Tracking Model |
Abstract: Entity detection and tracking (EDT) is the task of identifying textual mentions of real-world entities in documents, extending the named entity detection and coreference resolution task by considering mentions other than names (pronouns, definite descriptions, etc.). Like NE tagging and coreference resolution... |
Title: A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior |
Abstract: We develop a Bayesian framework for tackling the supervised clustering problem, the generic problem encountered in tasks such as reference matching, coreference resolution, identity uncertainty and record linkage. Our clustering model is based on the Dirichlet process prior, which enables us to define distrib... |
Title: Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction |
Abstract: Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., linear chains) in which search and parameter estimation can be performed exactly. Unfortunately, in many complex problems, it is rare... |
Title: The Soft Cumulative Constraint |
Abstract: This research report presents an extension of Cumulative of Choco constraint solver, which is useful to encode over-constrained cumulative problems. This new global constraint uses sweep and task interval violation-based algorithms. |
Title: Apply Local Clustering Method to Improve the Running Speed of Ant Colony Optimization |
Abstract: Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting down running time is one of study focuses, and one way is to decrease p... |
Title: Visualizing Topics with Multi-Word Expressions |
Abstract: We describe a new method for visualizing topics, the distributions over terms that are automatically extracted from large text corpora using latent variable models. Our method finds significant $n$-grams related to a topic, which are then used to help understand and interpret the underlying distribution. Comp... |
Title: Learning Gaussian Mixtures with Arbitrary Separation |
Abstract: In this paper we present a method for learning the parameters of a mixture of $k$ identical spherical Gaussians in $n$-dimensional space with an arbitrarily small separation between the components. Our algorithm is polynomial in all parameters other than $k$. The algorithm is based on an appropriate grid sear... |
Title: Design of an Optimal Bayesian Incentive Compatible Broadcast Protocol for Ad hoc Networks with Rational Nodes |
Abstract: Nodes in an ad hoc wireless network incur certain costs for forwarding packets since packet forwarding consumes the resources of the nodes. If the nodes are rational, free packet forwarding by the nodes cannot be taken for granted and incentive based protocols are required to stimulate cooperation among the n... |
Title: Self-Assembling Systems are Distributed Systems |
Abstract: In 2004, Klavins et al. introduced the use of graph grammars to describe -- and to program -- systems of self-assembly. We show that these graph grammars can be embedded in a graph rewriting characterization of distributed systems that was proposed by Degano and Montanari over twenty years ago. We apply this ... |
Title: Riemannian Manifold Hamiltonian Monte Carlo |
Abstract: The paper proposes a Riemannian Manifold Hamiltonian Monte Carlo sampler to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The method provides a fully automated adaptation mechanism that circumvents ... |
Title: How Controlled English can Improve Semantic Wikis |
Abstract: The motivation of semantic wikis is to make acquisition, maintenance, and mining of formal knowledge simpler, faster, and more flexible. However, most existing semantic wikis have a very technical interface and are restricted to a relatively low level of expressivity. In this paper, we explain how AceWiki use... |
Title: Adaptive Multiple Importance Sampling |
Abstract: The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earlier adaptive importance sampling implementations like Population Monte Carlo is that the importance weights of all simulated values... |
Title: Privacy constraints in regularized convex optimization |
Abstract: This paper is withdrawn due to some errors, which are corrected in arXiv:0912.0071v4 [cs.LG]. |
Title: A new protein binding pocket similarity measure based on comparison of 3D atom clouds: application to ligand prediction |
Abstract: Motivation: Prediction of ligands for proteins of known 3D structure is important to understand structure-function relationship, predict molecular function, or design new drugs. Results: We explore a new approach for ligand prediction in which binding pockets are represented by atom clouds. Each target pocket... |
Title: Augmenting Light Field to model Wave Optics effects |
Abstract: The ray-based 4D light field representation cannot be directly used to analyze diffractive or phase--sensitive optical elements. In this paper, we exploit tools from wave optics and extend the light field representation via a novel "light field transform". We introduce a key modification to the ray--based mod... |
Title: Towards the quantification of the semantic information encoded in written language |
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