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Title:

Robust Multi-Task Learning with t-Processes

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Most current multi-task learning frameworks ignore the robustness issue, which means that the presence of “outlier ” tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, t-processes (TP), which are a generalization of Gaussian processes (GP) for multi-task learning. TP allows the s...

Most current multi-task learning frameworks ignore the robustness issue, which means that the presence of “outlier ” tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, t-processes (TP), which are a generalization of Gaussian processes (GP) for multi-task learning. TP allows the system to effectively distinguish good tasks from noisy or outlier tasks. Experiments show that TP not only improves overall system performance, but can also serve as an indicator for the “informativeness ” of different tasks. 1. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://wwwbrauer.informatik.tu-muenchen.de/~trespvol/papers/icml2007_tp.pdf

http://wwwbrauer.informatik.tu-muenchen.de/~trespvol/papers/icml2007_tp.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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Title:

Blockwise supervised inference on large graphs

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In this paper we consider supervised learning on large-scale graphs, which is highly demanding in terms of time and memory costs. We demonstrate that, if a graph has a bipartite structure that contains a small set of nodes separating the remaining from each other, the inference can be equivalently done over an induced graph connecting only the s...

In this paper we consider supervised learning on large-scale graphs, which is highly demanding in terms of time and memory costs. We demonstrate that, if a graph has a bipartite structure that contains a small set of nodes separating the remaining from each other, the inference can be equivalently done over an induced graph connecting only the separators. Since each separator influences a certain neighborhood, the method essentially explores the block structure of graphs to improve the scalability. In the next step, instead of identifying the bipartite structure in a given graph, which is often difficult, we propose to construct a set of separators via two methods, one is adjacency matrix factorization and the other is mixture models, which both naturally ends up with a bipartite graph and meanwhile preserves the original data structure. Finally we report results of experiments on a toy problem and an intrusion detection problem. 1. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-17

Source:

http://wwwbrauer.informatik.tu-muenchen.de/~trespvol/papers/block_graph_inference.pdf

http://wwwbrauer.informatik.tu-muenchen.de/~trespvol/papers/block_graph_inference.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Blockwise supervised inference on large graphs

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In this paper we consider supervised learning on large-scale graphs, which is highly demanding in terms of time and memory costs. We demonstrate that, if a graph has a bipartite structure that contains a small set of nodes separating the remaining from each other, the inference can be equivalently done over an induced graph connecting only the s...

In this paper we consider supervised learning on large-scale graphs, which is highly demanding in terms of time and memory costs. We demonstrate that, if a graph has a bipartite structure that contains a small set of nodes separating the remaining from each other, the inference can be equivalently done over an induced graph connecting only the separators. Since each separator influences a certain neighborhood, the method essentially explores the block structure of graphs to improve the scalability. In the next step, instead of identifying the bipartite structure in a given graph, which is often difficult, we propose to construct a set of separators via two methods, one is adjacency matrix factorization and the other is mixture models, which both naturally ends up with a bipartite graph and meanwhile preserves the original data structure. Finally we report results of experiments on a toy problem and an intrusion detection problem. 1. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-17

Source:

http://www.dbs.informatik.uni-muenchen.de/~yu_k/block_graph_inference.pdf

http://www.dbs.informatik.uni-muenchen.de/~yu_k/block_graph_inference.pdf Minimize

Document Type:

text

Language:

en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Knowledge

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Social networks usually involve rich collections of objects, which are jointly linked into complex relational networks. Social network analysis has gained in importance due to the growing availability of data on novel social networks, e.g. citation networks, Web 2.0 social networks like facebook, and the hyperlinked internet. Recently, the infin...

Social networks usually involve rich collections of objects, which are jointly linked into complex relational networks. Social network analysis has gained in importance due to the growing availability of data on novel social networks, e.g. citation networks, Web 2.0 social networks like facebook, and the hyperlinked internet. Recently, the infinite hidden relational model (IHRM) has been developed for the analysis of complex relational domains. The IHRM extends the expressiveness of a relational model by introducing for each object an infinite-dimensional hidden variable as part of a Dirichlet process mixture model. In this paper we discuss how the IHRM can be used to model and analyze social networks. In such an IHRM-based social network model, each edge is associated with a random variable (RV) and the probabilistic dependencies between these RVs are specified by the model Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-07

Source:

http://wwwbrauer.informatik.tu-muenchen.de/~trespvol/papers/snakdd08srl.pdf

http://wwwbrauer.informatik.tu-muenchen.de/~trespvol/papers/snakdd08srl.pdf Minimize

Document Type:

text

Language:

en

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Title:

1 VIPS: a Vision-based Page Segmentation Algorithm

Author:

Deng Cai ; Shipeng Yu ; Ji-rong Wen ; Wei-ying Ma ; Deng Cai ; Shipeng Yu ; Ji-rong Wen ; Wei-ying Ma

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A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic top-down, tag-tree independent approach to detect web content structure. It simu...

A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic top-down, tag-tree independent approach to detect web content structure. It simulates how a user understands web layout structure based on his visual perception. Comparing to other existing techniques, our approach is independent to underlying documentation representation such as HTML and works well even when the HTML structure is far different from layout structure. Experiments show satisfactory results. 1 Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-08-15

Source:

http://research.microsoft.com/~jrwen/jrwen_files/publications/vips_technical report.pdf

http://research.microsoft.com/~jrwen/jrwen_files/publications/vips_technical report.pdf Minimize

Document Type:

text

Language:

en

Subjects:

VIPS ; a Vision-based Page Segmentation Algorithm

VIPS ; a Vision-based Page Segmentation Algorithm Minimize

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Title:

Stochastic relational models for discriminative link prediction

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We introduce a Gaussian process (GP) framework, stochastic relational models (SRM), for learning social, physical, and other relational phenomena where interactions between entities are observed. The key idea is to model the stochastic structure of entity relationships (i.e., links) via a tensor interaction of multiple GPs, each defined on one t...

We introduce a Gaussian process (GP) framework, stochastic relational models (SRM), for learning social, physical, and other relational phenomena where interactions between entities are observed. The key idea is to model the stochastic structure of entity relationships (i.e., links) via a tensor interaction of multiple GPs, each defined on one type of entities. These models in fact define a set of nonparametric priors on infinite dimensional tensor matrices, where each element represents a relationship between a tuple of entities. By maximizing the marginalized likelihood, information is exchanged between the participating GPs through the entire relational network, so that the dependency structure of links is messaged to the dependency of entities, reflected by the adapted GP kernels. The framework offers a discriminative approach to link prediction, namely, predicting the existences, strengths, or types of relationships based on the partially observed linkage network as well as the attributes of entities (if given). We discuss properties and variants of SRM and derive an efficient learning algorithm. Very encouraging experimental results are achieved on a toy problem and a user-movie preference link prediction task. In the end we discuss extensions of SRM to general relational learning tasks. 1 Minimize

Publisher:

MIT Press

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-01-06

Source:

http://books.nips.cc/papers/files/nips19/NIPS2006_0452.pdf

http://books.nips.cc/papers/files/nips19/NIPS2006_0452.pdf Minimize

Document Type:

text

Language:

en

DDC:

006 Special computer methods *(computed)*

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Title:

Multi-Label Informed Latent Semantic Indexing

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Latent semantic indexing (LSI) is a well-known unsupervised approach for dimensionality reduction in information retrieval. However if the output information (i.e. category labels) is available, it is often beneficial to derive the indexing not only based on the inputs but also on the target values in the training data set. This is of particular...

Latent semantic indexing (LSI) is a well-known unsupervised approach for dimensionality reduction in information retrieval. However if the output information (i.e. category labels) is available, it is often beneficial to derive the indexing not only based on the inputs but also on the target values in the training data set. This is of particular importance in applications with multiple labels, in which each document can belong to several categories simultaneously. In this paper we introduce the multi-label informed latent semantic indexing (MLSI) algorithm which preserves the information of inputs and meanwhile captures the correlations between the multiple outputs. The recovered “latent semantics” thus incorporate the human-annotated category information and can be used to greatly improve the prediction accuracy. Empirical study based on two data sets, Reuters-21578 and RCV1, demonstrates very encouraging results. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-12-10

Source:

http://www.dbs.informatik.uni-muenchen.de/~spyu/paper/SIGIR05_MLSI.pdf

http://www.dbs.informatik.uni-muenchen.de/~spyu/paper/SIGIR05_MLSI.pdf Minimize

Document Type:

text

Language:

en

Subjects:

H.3 [Information Storage and Retrieval ; Content Analysis and Indexing—Indexing methods General Terms Algorithms ; Theory ; Measurement ; Performance Keywords Latent Semantic Indexing ; Dimensionality Reduction ; Supervised Projection ; Multi-label Classification

H.3 [Information Storage and Retrieval ; Content Analysis and Indexing—Indexing methods General Terms Algorithms ; Theory ; Measurement ; Performance Keywords Latent Semantic Indexing ; Dimensionality Reduction ; Supervised Projection ; Multi-label Classification Minimize

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006 Special computer methods *(computed)*

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Title:

Local Learning Projections

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This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the projection that has the minimal global estimation error. Then we propose a dimensionality reduction algorithm that leads to the projection with the min...

This paper presents a Local Learning Projection (LLP) approach for linear dimensionality reduction. We first point out that the well known Principal Component Analysis (PCA) essentially seeks the projection that has the minimal global estimation error. Then we propose a dimensionality reduction algorithm that leads to the projection with the minimal local estimation error, and elucidate its advantages for classification tasks. We also indicate that LLP keeps the local information in the sense that the projection value of each point can be well estimated based on its neighbors and their projection values. Experimental results are provided to validate the effectiveness of the proposed algorithm. 1. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://imls.engr.oregonstate.edu/www/htdocs/proceedings/icml2007/papers/377.pdf

http://imls.engr.oregonstate.edu/www/htdocs/proceedings/icml2007/papers/377.pdf Minimize

Document Type:

text

Language:

en

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Title:

Siemens Medical Solutions, USA

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of growing interest in machine learning. Xu et al. (2006) introduced the infinite hidden relational model

of growing interest in machine learning. Xu et al. (2006) introduced the infinite hidden relational model Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://tresp.org/papers/ihrm_mlg07.pdf

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Document Type:

text

Language:

en

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Title:

Collaborative Ordinal Regression

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Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks need to be handled simultaneously. Rather than modelling each task individually, we build a hierarchical ...

Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks need to be handled simultaneously. Rather than modelling each task individually, we build a hierarchical Bayesian model and assign a common Gaussian Process (GP) prior to all individual latent functions. This very general model allows us to formally model the inter-dependencies between regression functions. We derive a very general learning scheme for this type of models, and in particular we evaluate two example models with collaborative effect. Empirical studies show that collaborative model outperforms the individual counterpart. 1 Minimize

Publisher:

ACM Press

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-17

Source:

http://www.dbs.informatik.uni-muenchen.de/~spyu/paper/nips05-rankingworkshop.pdf

http://www.dbs.informatik.uni-muenchen.de/~spyu/paper/nips05-rankingworkshop.pdf Minimize

Document Type:

text

Language:

en

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