@inproceedings{Ning2011c,
author = {\textbf{X. Ning} and Karypis, George},
title = {SLIM: Sparse Linear Models for Top-N Recommender Systems},
booktitle = {IEEE International Conference on Data Mining},
year = {2011},
note = {acceptance rate: 12\%},
abstract = {This paper focuses on developing effective and efficient algorithms
for top-N recommender systems. A novel Sparse LInear Method (SLIM)
is proposed, which generates top-N recommendations by aggregating
from user purchase/rating profiles. A sparse aggregation coefficient
matrix W is learned from SLIM by solving an L1-norm and L2-norm regularized
optimization problem. W is demonstrated to produce high-quality recommendations
and its sparsity allows SLIM to generate recommendations very fast.
A comprehensive set of experiments is conducted by comparing the
SLIM method and other state-of-the-art top-N recommendation methods.
The experiments show that SLIM achieves significant improvements
both in run time performance and recommendation quality over the
best existing methods.},
file = {papers/Ning2011c.pdf},
keywords = {Top-N Recommender Systems, Sparse Linear Methods, L1-norm Regularization},
owner = {xia},
timestamp = {2011.03.27}
}
@inproceedings{Ning2012a,
author = {\textbf{X. Ning} and Karypis, George},
title = {Sparse Linear Models with Side-information for Top-N Recommender
Systems},
booktitle = {Proceeding of WWW2012},
abstract = {This paper focuses on developing effctive algorithms that
utilize side information for top-N recommender systems. A
set of Sparse Linear Methods with Side information (SSLIM)
is proposed, that utilize a regularized optimization process
to learn a sparse item-to-item coefficient matrix based on
historical user-item purchase profiles and side information
associated with the items. This coefficient matrix is used
within an item-based recommendation framework to generate
a size-N ranked list of items for a user. Our experimental
results demonstrate that SSLIM outperforms other methods
in effectively utilizing side information and achieving performance
improvement.},
file = {papers/Ning2012a.pdf},
year = {2012},
owner = {xia},
timestamp = {2011.03.27}
}
@inproceedings{Ning2012b,
author = {\textbf{X. Ning} and Karypis, George},
title = {Sparse Linear Models with Side-information for Top-N Recommender
Systems},
booktitle = {Proceeding of RecSys2012},
year = {2012},
owner = {xia},
timestamp = {2011.03.27}
}
@inproceedings{Ning2010,
author = {\textbf{X. Ning} and George Karypis},
title = {Multi-task Learning for Recommender Systems},
booktitle = {Journal of Machine Learning Research Workshop and Conference Proceedings
(ACML2010)},
year = {2010},
volume = {13},
pages = {269-284},
publisher = {Microtome Publishing},
note = {acceptance rate: 30\%},
abstract = {This paper focuses on exploring personalized multi-task learning approaches
for collaborative filtering towards the goal of improving the prediction
performance of rating prediction systems. These methods first specifically
identify a set of users that are closely related to the user under
consideration (i.e., active user), and then learn multiple rating
prediction models simultaneously, one for the active user and one
for each of the related users. Such learning for multiple models
(tasks) in parallel is implemented by representing all learning instances
(users and items) using a coupled user-item representation, and within
error-insensitive Support Vector Regression (e-SVR) framework
applying multi-task kernel tricks. A comprehensive set of experiments
shows that multi-task learning approaches lead to significant performance
improvement over conventional alternatives.},
file = {papers/Ning2010.pdf},
keywords = {Collaborative Filtering, Multi-Task Learning},
owner = {xia},
timestamp = {2011.03.27}
}
@inproceedings{Ning2009a,
author = {\textbf{X. Ning} and George Karypis},
title = {The Set Classification Problem and Solution Methods},
booktitle = {SIAM International Conference on Data Mining},
year = {2009},
pages = {847-858},
publisher = {SIAM},
note = {acceptance rate: 16\%},
abstract = {This paper focuses on developing classification algorithms for problems
in which there is a need to predict the class based on multiple observations
(examples) of the same phenomenon (class). These problems give rise
to a new classification problem, referred to as set classification,
that requires the prediction of a set of instances given the prior
knowledge that all the instances of the set belong to the same unknown
class. This problem falls under the general class of problems whose
instances have class label dependencies. Four methods for solving
the set classification problem are developed and studied. The first
is based on a straightforward extension of the traditional classification
paradigm whereas the other three are designed to explicitly take
into account the known dependencies among the instances of the unlabeled
set during learning or classification. A comprehensive experimental
evaluation of the various methods and their underlying parameters
shows that some of them lead to significant gains in performance.},
bibsource = {DBLP, http://dblp.uni-trier.de},
ee = {http://www.siam.org/proceedings/datamining/2009/dm09_077_ningx.pdf},
file = {papers/Ning2009a.pdf}
}
@inproceedings{Ning2011,
author = {\textbf{X. Ning} and Qi, Yanjun},
title = {Semi-Supervised Convolution Graph Kernels for Relation Extraction},
booktitle = {SIAM International Conference on Data Mining},
year = {2011},
note = {acceptance rate: 25\%},
abstract = {Extracting semantic relations between entities is an important step
towards automatic text understanding. In this paper, we propose a
novel Semi-supervised Convolution Graph Kernel (SCGK) method for
semantic Relation Extraction
(RE) from natural English text. By encoding sentences as dependency
graphs of words, SCGK computes kernels (similarities) between sentences
using a convolution strategy, i.e., calculating similarities over
all possible short single paths on two dependency graphs. Furthermore,
SCGK adds three semi-supervised strategies in the kernel calculation
to enable soft-matching between (1) words, (2) grammatical dependencies,
and (3) entire sentences, respectively. From a large unannotated
corpus, these semi-supervision steps learn to capture contextual
semantic patterns of elements inside natural sentences, and therefore
alleviate the lack of annotated examples in most RE corpora. Through
convolutions and multi-level semi-supervisions, SCGK provides a powerful
model to encode both syntactic and semantic evidence which are important
for effectively recovering the relational patterns of interest. We
perform extensive experiments on five RE benchmark datasets which
aim to identify interaction relationships from biomedical literature.
Our results demonstrate that SCGK achieves the state-of-the-art performance
on the task of semantic relation extraction.},
file = {papers/Ning2011.pdf},
keywords = {Relation Extraction, Graph Kernels, Semisupervised Learning, Natural
Language Processing},
owner = {xia},
timestamp = {2011.03.27}
}
@inproceedings{Ning2011d,
author = {\textbf{X. Ning}, Michael Walters and Karypis, George},
title = {Improved Machine Learning Models for Predicting Selective Compounds},
booktitle = {ACM Conference on Bioinformatics, Computational Biology and Biomedicine},
year = {2011},
note = {acceptance rate: 19\%},
abstract = {The identification of small potent compounds that selectively bind
to the target under consideration with high affinities is a critical
step towards successful drug discovery. However, there still lacks
efficient and accurate computational methods to predict compound
selectivity properties. In this paper, we propose a set of machine
learning methods to do compound selectivity prediction. In particular,
we propose a novel cascaded learning method and a multi-task learning
method. The cascaded method decomposes the selectivity prediction
into two steps, one model for each step, so as to effectively filter
out non-selective compounds. The multi-task method incorporates both
activity and selectivity models into one multi-task model so as to
better differentiate compound selectivity properties. We conducted
a comprehensive set of experiments and compared the results with
other conventional selectivity prediction methods, and our results
demonstrated that the cascaded and multi-task methods significantly
improve the selectivity prediction performance.},
file = {papers/Ning2011d.pdf},
owner = {xia},
timestamp = {2011.03.27}
}
@article{Chen2005,
author = {Jianjun Chen and Yao Zheng and \textbf{X. Ning}},
title = {Scalable Parallel Quadrilateral Mesh Generation Coupled with Mesh
Partitioning},
journal = {International Conference on Parallel and Distributed Computing Applications
and Technologies},
year = {2005},
pages = {966-970},
note = {acceptance rate: 25\%},
abstract = {In this paper, we present our efforts to parallelize an unstructured
quadrilateral mesh generator. Its serial version is based on the
divider-and-conquer idea, and mainly includes two stages, i.e. geometry
decomposition and mesh generation. Both stages are parallelized separately.
A highly efficient fine-grain level parallel scheme is presented
to parallelize the stage of geometry decomposition. A SubDomain Graph
(SDG), which represents the connections of subdomains, is constructed.
The task of parallel mesh generation is then reduced to that of the
SDG partitioning.
Since the number of elements in subdomains could be pre-computed before
meshing, a static load balancing scheme to partition the SDG performs
well with the aid of Metis tools. Numerical results show that scalable
timing performance could be achieved by using the parallel mesh generator
with resulting meshes nicely partitioned among processors, which
enables a fast parallel simulation environment by eliminating the
traditional I/O-busy process of mesh repartitioning.},
address = {Los Alamitos, CA, USA},
doi = {http://doi.ieeecomputersociety.org/10.1109/PDCAT.2005.210},
file = {papers/Chen2005.pdf},
isbn = {0-7695-2405-2},
publisher = {IEEE Computer Society}
}
@inproceedings{Kuksa2010,
author = {Kuksa, Pavel and Qi, Yanjun and Bai, Bing and Collobert, Ronan and
Weston, Jason and Pavlovic, Vladimir and \textbf{X. Ning}},
title = {Semi-supervised abstraction-augmented string kernel for multi-level
bio-relation extraction},
booktitle = {Proceedings of the 2010 European conference on Machine learning and
knowledge discovery in databases: Part II},
year = {2010},
pages = {128--144},
address = {Berlin, Heidelberg},
publisher = {Springer-Verlag},
note = {acceptance rate: 17\%},
abstract = {Bio-relation extraction (bRE), an important goal in bio-text mining,
involves subtasks identifying relationships between bio-entities
in text at multiple levels, e.g., at the article, sentence or relation
level.A key limitation of current bRE systems is that they are restricted
by the availability of annotated corpora. In this work we introduce
a semi-supervised approach that can tackle multi-level bRE via string
compar-isons with mismatches in the string kernel framework. Our
string kernel implements an abstraction step, which groups similar
words to gener-ate more abstract entities, which can be learnt with
unlabeled data.Speci¯cally, two unsupervised models are proposed
to capture contex-tual (local or global) semantic similarities between
words from a large unannotated corpus. This Abstraction-augmented
String Kernel (ASK) allows for better generalization of patterns
learned from annotated data and provides a uniffied framework for
solving bRE with multiple degrees of detail. ASK shows effective
improvements over classic string kernels on four datasets and achieves
state-of-the-art bRE performance without the need for complex linguistic
features.},
acmid = {1888315},
isbn = {3-642-15882-X, 978-3-642-15882-7},
keywords = {learning with auxiliary information, relation extraction, semi-supervised
string kernel, sequence classification},
location = {Barcelona, Spain},
numpages = {17},
file = {papers/Kuksa2010.pdf}
}
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