the Bethe approximation, and corresponding generalized belief propagation (GBP) algorithms. We emphasize the conditions a free energy approximation must satisfy in order to be a “valid ” or “maxent-normal ” approximation. We describe the relationship between four different methods that can be used

In this paper, we devise cell-based maximum-entropy (max-ent) basis functions that are used in a Galerkin method for the solution of partial differential equations. The motivation behind this work is the construction of smooth approximants with controllable support on unstructured meshes

graphs derived from EP have a number of good structural properties, including maxent-normality and overall counting number of one. The result is a convenient framework for producing high-quality approximations with a user-adjustable level of complexity. 1

We investigate the problem of generating fast approximate answers to queries for large sparse binary data sets. We focus in particular on probabilistic model-based approaches to this problem and develop a number of techniques that are significantly more accurate than a baseline independence model

In this thesis I examine one commonly used class of methods for the analytic approximation of cellular automata, the so-called local cluster approxima-tions. This class subsumes the well known mean-field and pair approxima-tions, as well as higher order generalizations of these. While a straightfor

the variables. We present new approaches to approximate inference based on linear programming (LP) relaxations. Our algorithms optimize over the cycle relaxation of the marginal polytope, which we show to be closely related to the first lifting of the Sherali-Adams hierarchy, and is significantly tighter than

Entity detection and tracking is a relatively new addition to the repertoire of natural language tasks. In this paper, we present a statistical language-independent framework for identifying and tracking named, nominal and pronominal references to entities within unrestricted text documents, and chaining them into clusters corresponding to each logical entity present in the text. Both the mention detection model and the novel entity tracking model can use arbitrary feature types, being able to integrate a wide array of lexical, syntactic and semantic features. In addition, the mention detection model crucially uses feature streams derived from different named entity classifiers. The proposed framework is evaluated with several experiments run in Arabic, Chinese and English texts; a system based on the approach described here and submitted to the latest Automatic Content Extraction (ACE) evaluation achieved top-tier results in all three evaluation languages. 1

This study proposes an information-theoretic deconvolution method to approximate the entire distribution of individual treatment effect. This method uses higher-order information implied by the standard average treatment effect estimator to approximate the underlying distribution of individual

New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline.

Abstract not found