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421
Trust region Newton method for largescale logistic regression
 In Proceedings of the 24th International Conference on Machine Learning (ICML
, 2007
"... Largescale logistic regression arises in many applications such as document classification and natural language processing. In this paper, we apply a trust region Newton method to maximize the loglikelihood of the logistic regression model. The proposed method uses only approximate Newton steps in ..."
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Cited by 64 (10 self)
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Largescale logistic regression arises in many applications such as document classification and natural language processing. In this paper, we apply a trust region Newton method to maximize the loglikelihood of the logistic regression model. The proposed method uses only approximate Newton steps in the beginning, but achieves fast convergence in the end. Experiments show that it is faster than the commonly used quasi Newton approach for logistic regression. We also compare it with existing linear SVM implementations. 1
Supervised Random Walks: Predicting and Recommending Links in Social Networks
"... Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Althoug ..."
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Cited by 56 (0 self)
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Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open. We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function. Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms stateoftheart unsupervised approaches as well as approaches that are based on feature extraction.
Painless Unsupervised Learning with Features
"... We show how features can easily be added to standard generative models for unsupervised learning, without requiring complex new training methods. In particular, each component multinomial of a generative model can be turned into a miniature logistic regression model if feature locality permits. The ..."
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Cited by 54 (3 self)
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We show how features can easily be added to standard generative models for unsupervised learning, without requiring complex new training methods. In particular, each component multinomial of a generative model can be turned into a miniature logistic regression model if feature locality permits. The intuitive EM algorithm still applies, but with a gradientbased Mstep familiar from discriminative training of logistic regression models. We apply this technique to partofspeech induction, grammar induction, word alignment, and word segmentation, incorporating a few linguisticallymotivated features into the standard generative model for each task. These featureenhanced models each outperform their basic counterparts by a substantial margin, and even compete with and surpass more complex stateoftheart models. 1
Superresolution Enhancement of Text Image Sequences
, 2000
"... The objective of this work is the superresolution enhancement of image sequences. We consider in particular images of scenes for which the pointtopoint image transformation is a plane projective transformation. We first describe the imaging model, and a maximum likelihood (ML) estimator of the s ..."
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Cited by 48 (2 self)
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The objective of this work is the superresolution enhancement of image sequences. We consider in particular images of scenes for which the pointtopoint image transformation is a plane projective transformation. We first describe the imaging model, and a maximum likelihood (ML) estimator of the superresolution image. We demonstrate the extreme noise sensitivity of the unconstrained ML estimator. We show that the Irani and Peleg [9, 10] superresolution algorithm does not suffer from this sensitivity, and explain that this stability is due to the error backprojection method which effectively constrains the solution. We then propose two estimators suitable for the enhancement of text images: a maximum a posterior (MAP) estimator based on a Huber prior, and an estimator regularized using the Total Variation norm. We demonstrate the improved noise robustness of these approaches over the Irani and Peleg estimator. We also show the effects of a poorly estimated point spread function (PS...
Efficient, featurebased, conditional random field parsing
 In Proc. ACL/HLT
, 2008
"... Discriminative featurebased methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior featurebased dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first gene ..."
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Cited by 43 (4 self)
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Discriminative featurebased methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior featurebased dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data. Our efficiency is primarily due to the use of stochastic optimization techniques, as well as parallelization and chart prefiltering. On WSJ15, we attain a stateoftheart Fscore of 90.9%, a 14 % relative reduction in error over previous models, while being two orders of magnitude faster. On sentences of length 40, our system achieves an Fscore of 89.0%, a 36 % relative reduction in error over a generative baseline. 1
Geometric modeling in shape space
 In Proc. SIGGRAPH
, 2007
"... Figure 1: Geodesic interpolation and extrapolation. The blue input poses of the elephant are geodesically interpolated in an asisometricaspossible fashion (shown in green), and the resulting path is geodesically continued (shown in purple) to naturally extend the sequence. No semantic information, ..."
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Cited by 41 (4 self)
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Figure 1: Geodesic interpolation and extrapolation. The blue input poses of the elephant are geodesically interpolated in an asisometricaspossible fashion (shown in green), and the resulting path is geodesically continued (shown in purple) to naturally extend the sequence. No semantic information, segmentation, or knowledge of articulated components is used. We present a novel framework to treat shapes in the setting of Riemannian geometry. Shapes – triangular meshes or more generally straight line graphs in Euclidean space – are treated as points in a shape space. We introduce useful Riemannian metrics in this space to aid the user in design and modeling tasks, especially to explore the space of (approximately) isometric deformations of a given shape. Much of the work relies on an efficient algorithm to compute geodesics in shape spaces; to this end, we present a multiresolution framework to solve the interpolation problem – which amounts to solving a boundary value problem – as well as the extrapolation problem – an initial value problem – in shape space. Based on these two operations, several classical concepts like parallel transport and the exponential map can be used in shape space to solve various geometric modeling and geometry processing tasks. Applications include shape morphing, shape deformation, deformation transfer, and intuitive shape exploration.
On the implementation of an algorithm for largescale equality constrained optimization
 SIAM Journal on Optimization
, 1998
"... Abstract. This paper describes a software implementation of Byrd and Omojokun’s trust region algorithm for solving nonlinear equality constrained optimization problems. The code is designed for the efficient solution of large problems and provides the user with a variety of linear algebra techniques ..."
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Cited by 38 (11 self)
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Abstract. This paper describes a software implementation of Byrd and Omojokun’s trust region algorithm for solving nonlinear equality constrained optimization problems. The code is designed for the efficient solution of large problems and provides the user with a variety of linear algebra techniques for solving the subproblems occurring in the algorithm. Second derivative information can be used, but when it is not available, limited memory quasiNewton approximations are made. The performance of the code is studied using a set of difficult test problems from the CUTE collection.
Conditional random fields for activity recognition
 In Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2007
, 2007
"... of any sponsoring institution, the U.S. government or any other entity. ..."
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Cited by 38 (0 self)
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of any sponsoring institution, the U.S. government or any other entity.
LBFGSB  Fortran Subroutines for LargeScale Bound Constrained Optimization
, 1994
"... LBFGSB is a limited memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. It is intended for problems in which information on the Hessian matrix is di cult to obtain, or for large dense problems. LBFGSB can also be used for unconstrained pr ..."
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Cited by 38 (2 self)
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LBFGSB is a limited memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. It is intended for problems in which information on the Hessian matrix is di cult to obtain, or for large dense problems. LBFGSB can also be used for unconstrained problems, and in this case performs similarly to its predecessor, algorithm LBFGS (Harwell routine VA15). The algorithm is implemented in Fortran 77.