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CHU,
WEI
Short CV
@Google Scholar
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Welcome to my homepage. I am with Microsoft now. Previously I was an associate research scientist
of CCLS, Columbia University and then a scientist of Yahoo! Labs. Before I moved to US, I was a senior postdoctoral
fellow of
the Gatsby
Computational Neuroscience Unit, University College London,
working with
Zoubin
Ghahramani and David
L. Wild on Machine Learning and Bioinformatics. I
received Ph.D. degree at the Dept. of Mechanical
Engineering, National
University
of Singapore, under the joint guidance of S. Sathiya
Keerthi and
Chong
Jin Ong.
1. Recent Work
2.
Publications
3. Source Code
1. Recent Work
P. Bennett, R. White, W.
Chu, S. Dumais, P. Bailey, F. Borisyuk and X. Cui (2012) Modeling and measuring the impact of short and long-term behavior on search personalization, ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-35)
(View
Abstract)
User behavior provides many cues to improve the relevance of search results through personalization. One aspect of user behavior that provides especially strong signals for delivering better relevance is an individuals history of queries and clicked docu-ments. Previous studies have explored how short-term behavior or long-term behavior can be predictive of relevance. Ours is the first study to assess how short-term (session) behavior and long-term (historic) behavior interact, and how each may be used in isolation or in combination to optimally contribute to gains in relevance through search personalization.[pdf]
L. Li, W.
Chu, J. Langford, T. Moon, and X. Wang (2012) An unbiased offline evaluation of contextual bandit algorithms with generalized linear models, Journal of Machine Learning Research - Workshop and Conference Proceedings 26 (JMLR W&CP-26)
(View
Abstract)
Contextual bandit algorithms have become popular tools in online recommendation and advertising systems. Offline evaluation of the effectiveness of new algorithms in these applications is critical for protecting online user experiences but very challenging due to their ``partial-label'' nature. The purpose of this paper is two-fold. First, we review a recently proposed offline evaluation technique. Different from simulator-based approaches, the method is completely data-driven, is easy to adapt to different applications, and more importantly, provides provably unbiased evaluations. We argue for the wide use of this technique as standard practice when comparing bandit algorithms in real-life problems. Second, as an application of this technique, we compare and validate a number of new algorithms based on generalized linear models. Experiments using real Yahoo! data suggest substantial improvement over algorithms with linear models when the rewards are binary. [pdf]
W. Chu, M. Zinkevich, L. Li, A. Thomas, and B. Tseng (2011) Unbiased online active learning in data streams, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-17)
(View
Abstract)
Unlabeled samples can be intelligently selected for labeling
to minimize classification error. In many real-world applications,
a large number of unlabeled samples arrive in a
streaming manner, making it impossible to maintain all the
data in a candidate pool. In this work, we consider the unbiasedness property in the
sampling process, and design optimal instrumental distributions
to minimize the variance in the stochastic process.
Meanwhile, Bayesian linear classifiers with weighted maximum
likelihood are optimized online to estimate parameters. [pdf]
T. Moon, W.
Chu, L. Li, Z. Zheng, Y. Chang (2012) Online learning framework for refining recency search results with user click feedback, to appear in Transactions on Information Systems
(View
Abstract)
In this paper, we focus on recency search and study a number of algorithms to improve ranking results by leveraging user click feedback. Our contributions are three-fold. First, we use real search sessions collected in a random exploration bucket for \emph{reliable} offline evaluation of these algorithms, which provides an unbiased comparison across algorithms without online bucket tests. Second, we propose a re-ranking approach to improve search results for recency queries using user clicks. Third, our empirical comparison of a dozen algorithms on real-life search data suggests importance of a few algorithmic choices in these applications, including generalization across different query-document pairs, specialization to popular queries, and real-time adaptation of user clicks. [pdf]
L. Li, W. Chu, J. Langford and X. Wang (2011)
Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms,
in Proc. of ACM Web Search and Data Mining (WSDM-04) 297-306 (View Abstract)
In this paper, we introduce a replay
methodology for contextual bandit algorithm evaluation.
Different from simulator-based approaches, our method is
completely data-driven and very easy to adapt to different
applications. More importantly, our method can provide provably
unbiased evaluations. Our empirical results on a large-scale
news article recommendation dataset collected from Yahoo!
Front Page conform well with our theoretical results.
Furthermore, comparisons between our offline replay and online
bucket evaluation of several contextual bandit algorithms show
accuracy and effectiveness of our offline evaluation method. [pdf]
L. Li, W. Chu, J. Langford
and R. E. Schapire (2010) A contextual-bandit approach to personalized news article recommendation,
in Proc. of International World Wide Web Conference
(WWW-19) (View
Abstract)
Personalized web services strive to adapt their services (advertisements,
news articles, etc.) to individual users by making use of both content
and user information. Despite a few recent advances, this problem
remains challenging for at least two reasons. First, web service is
featured with dynamically changing pools of content, rendering
traditional collaborative filtering methods inapplicable. Second,
the scale of most web services of practical interest calls for solutions
that are both fast in learning and computation.
In this work, we model personalized recommendation of news articles
as a contextual bandit problem, a principled approach in which a
learning algorithm sequentially selects articles to serve users based on
contextual information about the users and articles, while simultaneously
adapting its article-selection strategy based on user-click feedback to
maximize total user clicks.
[pdf]
W. Chu and Z.
Ghahramani (2009) Probabilistic models for incomplete multi-dimensional arrays,
in Proc. of International Conference on Artificial Intelligence and Statistics
(AISTATS-12) (View
Abstract)
In multiway data, each sample is measured by multiple sets of
correlated attributes. We develop a probabilistic framework for
modeling structural dependency from partially observed
multi-dimensional array data, known as pTucker. Latent components
associated with individual array dimensions are jointly retrieved
while the core tensor is integrated out. The resulting algorithm
is capable of handling large-scale data sets. We verify the
usefulness of this approach by comparing against classical models
on applications to modeling amino acid fluorescence, collaborative
filtering and a number of benchmark multiway array data.
[pdf] [third-party pTucker code]
W. Chu and S.-T. Park (2009) Personalized recommendation on dynamic content using predictive bilinear
models,
in Proc. of International World Wide Web Conference
(WWW-18) (View
Abstract)
In Web-based services of dynamic content (such as news articles),
recommender systems face the difficulty of timely identifying new
items of high-quality and providing recommendations for new users.
We propose a feature-based machine learning approach to
personalized recommendation that is capable of handling the
cold-start issue effectively. The proposed framework is general and flexible
for other personalized tasks. The superior performance of our
approach is verified on a large-scale data set collected from the
Today-Module on Yahoo! Front Page, with comparison against six
competitive approaches.
[pdf]
[slides]
S.-T. Park and W. Chu (2009) Pairwise preference regression for cold-start recommendation, in Proc. of ACM Recommender Systems (RecSys-03)
(View
Abstract)
Recommender systems are widely used in online e-commerce applications to improve user engagement and then to increase revenue. A key challenge for recommender systems is providing high quality recommendation to users in ``cold-start" situations. We consider three types of cold-start problems: 1) recommendation on existing items for new users; 2) recommendation on new items for existing users; 3) recommendation on new items for new users. We propose predictive feature-based regression models that leverage all available information of users and items, such as user demographic information and item content features, to tackle cold-start problems. The resulting algorithms scale efficiently as a linear function of the number of observations. We verify the usefulness of our approach in three cold-start settings on the MovieLens and EachMovie datasets, by comparing with five alternatives including random, most popular, segmented most popular, and two variations of Vibes affinity algorithm widely used at Yahoo! for recommendation.
R. Silva, W. Chu and Z.
Ghahramani (2007) Hidden common cause relations in relational learning,
in Advances in Neural Information Processing Systems
(NIPS-20) (View
Abstract)
We consider the case when relationships are postulated to exist due to hidden common
causes. We discuss how the resulting graphical model differs from Markov
networks, and how it describes different types of real-world relational processes.
A Bayesian nonparametric classification model is built upon this graphical representation
and evaluated with several empirical studies.
GOTO Ricardo Silva's homepage for [pdf], [data] and [code]
K.
Yu and W. Chu (2007) Gaussian process models for link analysis and transfer learning,
in Advances in Neural Information Processing Systems
(NIPS-20) (View
Abstract)
P.
K. Shivaswamy, W. Chu and M.
Jansche (2007)
A
support vector approach to censored targets, in Proc. of IEEE
International Conference on Data Mining
(ICDM-07)
(View
Abstract)
Censored targets, such as the time to events in survival
analysis,
can generally be represented by intervals on the real line. In
this paper, we propose a novel support vector technique (named SVCR)
for
regression on censored targets. Interestingly,
this approach provides a general formulation for both standard
regression and binary classification tasks.
[pdf]
[longer
version]
W. Chu, V.
Sindhwani, Z. Ghahramani
and S. S.
Keerthi (2006) Relational
learning with Gaussian processes, in Advances in Neural
Information Processing Systems (NIPS-19) (View
Abstract)
Correlation between instances is often modelled via a kernel
function using input attributes of the instances. Relational
knowledge can further reveal additional pairwise correlations
between variables of interest. In this paper, we develop a class
of models which incorporates both reciprocal relational
information and input attributes using Gaussian process
techniques. This approach provides a novel non-parametric Bayesian
framework with a data-dependent prior for supervised learning
tasks. We also apply this framework to semi-supervised learning.
Experimental results on several real world data sets verify the
usefulness of this algorithm.
[pdf]
S.
K.
Shevade and W. Chu (2006) Minimum
enclosing spheres formulations for support vector ordinal regression,
in Proc. of IEEE International Conference on Data Mining
(ICDM-06):1054-1058 (View
Abstract)
We present two new support vector approaches for ordinal
regression.
These approaches find the concentric spheres with minimum volume that
contain most of the training samples.
[pdf]
V.
Sindhwani, W. Chu and S. S. Keerthi
(2007) Semi-supervised
Gaussian process classifiers, in
Proc. of International Joint Conferences on Artificial Intelligence
(IJCAI-20):1059-1064 (View
Abstract)
We consider the problem of utilizing unlabeled data for
Gaussian
process inference. Using a geometrically motivated data-dependent
prior, we propose a graph-based construction of semi-supervised
Gaussian processes. We demonstrate this approach empirically on several
classification problems.
[pdf]
S.
S. Keerthi and W. Chu (2005) A
matching pursuit approach to sparse Gaussian process regression,
in Advances in Neural Information Processing Systems (NIPS-18) (View
Abstract)
In this paper, we propose a new basis
selection criterion for building sparse GP regression
models that provides promising gains in accuracy as well as
efficiency over previous methods.
Our algorithm is much faster than that of Smola and Bartlett,
while, in generalization it greatly outperforms the
information gain approach proposed by Seeger et al, especially
on the quality of predictive distributions.
[ps]
[code]
W. Chu and Z. Ghahramani
(2005) Preference
learning with Gaussian processes, in Proc.
of International Conference on Machine Learning
(ICML-22):137-144 (View
Abstract)
In this paper, we propose a probabilistic kernel approach to
preference learning based on Gaussian processes. A new likelihood
function is proposed to capture the preference relations in the
Bayesian framework. The generalized formulation is also applicable to
tackle many multiclass problems.
[ps]
[code]
W. Chu and S. S. Keerthi
(2005) New
approaches to support vector ordinal regression, in
Proc. of International Conference on Machine Learning
(ICML-22):145-152 (View
Abstract)
In this paper, we propose two new support vector formulations
for
ordinal regression, which optimize multiple thresholds to define
parallel discriminant hyperplanes for the ordinal scales. Both
approaches guarantee that the thresholds are properly ordered at the
optimal solution.
[ps]
[code]
W. Chu and Z. Ghahramani
(2005) Gaussian
processes for ordinal regression, Journal of
Machine Learning Research 6(Jul):1019--1041 (View
Abstract)
In this paper, we present a probabilistic approach to ordinal
regression in Gaussian processes. In the Bayesian framework of
Gaussian processes, we propose a likelihood function for ordinal
variables that is a generalization of the probit function.
Two inference techniques, based on Laplace approximation and
expectation propagation respectively, are applied for model
selection.
[ps]
[code]
W. Chu, Z.
Ghahramani, F. Falciani, and D. L. Wild
(2005) Biomarker
discovery with Gaussian processes in microarray gene expression data,
Bioinformatics
2005(21):3385-3393 (View
Abstract)
In this paper, we describe a gene selection algorithm
based on Gaussian processes to discover consistent gene expression
patterns associated with ordinal clinical phenotypes. The
technique of automatic relevance determination is applied to
represent the significance level of the genes in a Bayesian framework.
[pdf]
[code]
W. Chu, Z.
Ghahramani and D.
L. Wild
(2004) A
graphical model for protein secondary structure
prediction,
in Proc. of International Conference on Machine Learning
(ICML-21):161-168 (View
Abstract)
In this paper, we present a graphical model that extends
segmental semi-Markov
models (SSMM) to exploit multiple sequence alignment profiles for
protein structure
prediction. A novel parameterized model is proposed as the likelihood
function
for the SSMM. By incorporating the information from long range
interactions in
beta-sheets, this model is capable of carrying out inference on contact
maps.
[pdf]
[webserver]
W. Chu,
S. S.
Keerthi and C.
J. Ong
(2004) Bayesian
support vector regression using a unified loss function,
IEEE Transactions on Neural Networks 15(1):29-44 (View
Abstract)
In this paper, we use soft insensitive loss function
in likelihood evaluation, and describe a Bayesian framework in a
stationary Gaussian process. Bayesian methods are used to implement
model adaptation, while keeping the merits of support vector
regression, such as quadratic programming and sparseness. Moreover,
confidence interval is provided in prediction.
[code]
3. Source Code
- Bayesian
support vector machines for regression and binary classification
- Gaussian
processes for ordinal regression
- Support
vector ordinal regression
- Preference
learning with Gaussian processes
- A matching pursuit approach to sparse Gaussian process regression
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email : chu dot wei at microsoft.com
2012.08.14
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gipoco.com
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gipoco.com
is neither affiliated with the authors of this page nor responsible
for its contents. This is a safe-cache copy of the original web site.