Jiayuan Huang

Alumni

Empirical Inference

Publications

7 results (View BibTeX file of all listed publications)

2009

Covariate shift and local learning by distribution matching

Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schölkopf, B.

In Dataset Shift in Machine Learning, pages: 131-160, (Editors: Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, N. D.), MIT Press, Cambridge, MA, USA, 2009 (inbook)

Abstract

Given sets of observations of training and test data, we consider the problem of re-weighting the training data such that its distribution more closely matches that of the test data. We achieve this goal by matching covariate distributions between training and test sets in a high dimensional feature space (specifically, a reproducing kernel Hilbert space). This approach does not require distribution estimation. Instead, the sample weights are obtained by a simple quadratic programming procedure. We provide a uniform convergence bound on the distance between the reweighted training feature mean and the test feature mean, a transductive bound on the expected loss of an algorithm trained on the reweighted data, and a connection to single class SVMs. While our method is designed to deal with the case of simple covariate shift (in the sense of Chapter ??), we have also found benefits for sample selection bias on the labels. Our correction procedure yields its greatest and most consistent advantages when the learning algorithm returns a classifier/regressor that is simpler" than the data might suggest.

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2009

Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schölkopf, B. Covariate shift and local learning by distribution matching In Dataset Shift in Machine Learning, pages: 131-160, (Editors: Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A. and Lawrence, N. D.), MIT Press, Cambridge, MA, USA, 2009 (inbook)

PDF Web [BibTex]

2007

Learning with Hypergraphs: Clustering, Classification, and Embedding

Zhou, D., Huang, J., Schölkopf, B.

In Advances in Neural Information Processing Systems 19, pages: 1601-1608, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract

We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest are more complex than pairwise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for our learning tasks however. Therefore we consider using hypergraphs instead to completely represent complex relationships among the objects of our interest, and thus the problem of learning with hypergraphs arises. Our main contribution in this paper is to generalize the powerful methodology of spectral clustering which originally operates on undirected graphs to hypergraphs, and further develop algorithms for hypergraph embedding and transductive classi¯cation on the basis of the spectral hypergraph clustering approach. Our experiments on a number of benchmarks showed the advantages of hypergraphs over usual graphs.

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2007

Zhou, D., Huang, J., Schölkopf, B. Learning with Hypergraphs: Clustering, Classification, and Embedding In Advances in Neural Information Processing Systems 19, pages: 1601-1608, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

PDF Web [BibTex]

Correcting Sample Selection Bias by Unlabeled Data

Huang, J., Smola, A., Gretton, A., Borgwardt, K., Schölkopf, B.

In Advances in Neural Information Processing Systems 19, pages: 601-608, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

Abstract

We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate corrections based on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Our method works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.

PDF Web [BibTex]

Huang, J., Smola, A., Gretton, A., Borgwardt, K., Schölkopf, B. Correcting Sample Selection Bias by Unlabeled Data In Advances in Neural Information Processing Systems 19, pages: 601-608, (Editors: B Schölkopf and J Platt and T Hofmann), MIT Press, Cambridge, MA, USA, 20th Annual Conference on Neural Information Processing Systems (NIPS), September 2007 (inproceedings)

PDF Web [BibTex]

2006

Information Marginalization on Subgraphs

Huang, J., Zhu, T., Rereiner, R., Zhou, D., Schuurmans, D.

In ECML/PKDD 2006, pages: 199-210, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Abstract

Real-world data often involves objects that exhibit multiple relationships; for example, papers and authors exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. papers), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world dataachieving good results in many problems.

Web DOI [BibTex]

2006

Huang, J., Zhu, T., Rereiner, R., Zhou, D., Schuurmans, D. Information Marginalization on Subgraphs In ECML/PKDD 2006, pages: 199-210, (Editors: Fürnkranz, J. , T. Scheffer, M. Spiliopoulou), Springer, Berlin, Germany, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 2006 (inproceedings)

Web DOI [BibTex]

2005

A Combinatorial View of Graph Laplacians

Huang, J.

(144), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2005 (techreport)

Abstract

Discussions about different graph Laplacian, mainly normalized and unnormalized versions of graph Laplacian, have been ardent with respect to various methods in clustering and graph based semi-supervised learning. Previous research on graph Laplacians investigated their convergence properties to Laplacian operators on continuous manifolds. There is still no strong proof on convergence for the normalized Laplacian. In this paper, we analyze different variants of graph Laplacians directly from the ways solving the original graph partitioning problem. The graph partitioning problem is a well-known combinatorial NP hard optimization problem. The spectral solutions provide evidence that normalized Laplacian encodes more reasonable considerations for graph partitioning. We also provide some examples to show their differences.

[BibTex]

2005

Huang, J. A Combinatorial View of Graph Laplacians (144), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2005 (techreport)

[BibTex]

Beyond Pairwise Classification and Clustering Using Hypergraphs

Zhou, D., Huang, J., Schölkopf, B.

(143), Max Planck Institute for Biological Cybernetics, August 2005 (techreport)

Abstract

In many applications, relationships among objects of interest are more complex than pairwise. Simply approximating complex relationships as pairwise ones can lead to loss of information. An alternative for these applications is to analyze complex relationships among data directly, without the need to first represent the complex relationships into pairwise ones. A natural way to describe complex relationships is to use hypergraphs. A hypergraph is a graph in which edges can connect more than two vertices. Thus we consider learning from a hypergraph, and develop a general framework which is applicable to classification and clustering for complex relational data. We have applied our framework to real-world web classification problems and obtained encouraging results.

PDF [BibTex]

Zhou, D., Huang, J., Schölkopf, B. Beyond Pairwise Classification and Clustering Using Hypergraphs (143), Max Planck Institute for Biological Cybernetics, August 2005 (techreport)

PDF [BibTex]

Learning from Labeled and Unlabeled Data on a Directed Graph

Zhou, D., Huang, J., Schölkopf, B.

In Proceedings of the 22nd International Conference on Machine Learning, pages: 1041 -1048, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, August 2005 (inproceedings)

Abstract

We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is considered. The time complexity of the algorithm derived from this framework is nearly linear due to recently developed numerical techniques. In the absence of labeled instances, this framework can be utilized as a spectral clustering method for directed graphs, which generalizes the spectral clustering approach for undirected graphs. We have applied our framework to real-world web classification problems and obtained encouraging results.

PostScript PDF [BibTex]

Zhou, D., Huang, J., Schölkopf, B. Learning from Labeled and Unlabeled Data on a Directed Graph In Proceedings of the 22nd International Conference on Machine Learning, pages: 1041 -1048, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, August 2005 (inproceedings)

PostScript PDF [BibTex]

Jiayuan Huang

2009

Covariate shift and local learning by distribution matching

2009

2007

Learning with Hypergraphs: Clustering, Classification, and Embedding

2007

Correcting Sample Selection Bias by Unlabeled Data

2006

Information Marginalization on Subgraphs

2006

2005

A Combinatorial View of Graph Laplacians

2005

Beyond Pairwise Classification and Clustering Using Hypergraphs

Learning from Labeled and Unlabeled Data on a Directed Graph

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