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Simon Shaolei Du, Yilin Liu, Boyi Chen and Lei Li (2014), "Maxios: Large Scale Nonnegative Matrix Factorization for Collaborative Filtering", In Neural Information Processing Systems, workshop on Distributed Machine Learning and Matrix Computations.
Abstract: Nonnegative matrix factorization proved useful in many applications, including collaborative filtering – from existing ratings data one would like to predict new product ratings by users. However, factorizing a user-product score matrix is computation and memory intensive. We propose Maxios, a novel approach to fill missing values for large scale and highly sparse matrices efficiently and ac- curately. We formulate the matrix-completion problem as weighted nonnegative matrix factorization. In addition, we develop distributed update rules using alter- nating direction method of multipliers. We have implemented the Maxios system on top of Spark, a distributed in-memory computation framework. Experiments on commercial clusters show that Maxios is competitive in terms of scalability and accuracy against the existing solutions on a variety of datasets.
BibTeX:
@inproceedings{du2014maxios,
  author = {Simon Shaolei Du and Yilin Liu and Boyi Chen and Lei Li},
  title = {Maxios: Large Scale Nonnegative Matrix Factorization for Collaborative Filtering},
  booktitle = {Neural Information Processing Systems, workshop on Distributed Machine Learning and Matrix Computations},
  year = {2014}
}
Da-Cheng Juan, Lei Li, Huan-Kai Peng, Diana Marculescu and Christos Faloutsos (2014), "Beyond Poisson: Modeling Inter-Arrival Times of Requests in a Datacenter", In The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
BibTeX:
@inproceedings{juan2014beyond,
  author = {Da-Cheng Juan and Lei Li and Huan-Kai Peng and Diana Marculescu and Christos Faloutsos},
  title = {Beyond Poisson: Modeling Inter-Arrival Times of Requests in a Datacenter},
  booktitle = {The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year = {2014}
}
Yi Wu, Lei Li and Stuart J. Russell (2014), "BFiT: From Possible-World Semantics to Random-Evaluation Semantics in Open Universe", In Neural Information Processing Systems, Probabilistic Programming workshop.
Abstract: In recent years, several probabilistic programming languages (PPLs) have emerged, such as Bayesian Logic (BLOG), Church, and Figaro. These languages can be classified into two categories: PPLs interpreted using possible-world se- mantics and ones using random-evaluation semantics. In this paper, we explic- itly analyze the equivalence between these two semantics in the context of open- universe probability models (OUPMs). We propose a novel dynamic memoization technique to construct OUPMs using procedural instructions in random-evaluation based PPLs. We implemented a translator named BFiT, which converts code in BLOG (possible-world based) to Figaro (random-evaluation based). The trans- lated program in Figaro exhibits a merely constant blowup factor in program size while yielding the same inference results as the original model in BLOG.
BibTeX:
@inproceedings{wu2014bfit,
  author = {Yi Wu and Lei Li and Stuart J. Russell},
  title = {BFiT: From Possible-World Semantics to Random-Evaluation Semantics in Open Universe},
  booktitle = {Neural Information Processing Systems, Probabilistic Programming workshop},
  year = {2014}
}
Yusuf Erol, Lei Li, Bharath Ramsundar and Stuart J. Russell (2013), "The Extended Parameter Filter", In Proceedings of the 30th International Conference on Machine learning.

The full version appeared as Tech. Rep. UCB/EECS-2013-48.

Abstract: The parameters of temporal models, such as dynamic Bayesian networks, may be modelled in a Bayesian context as static or atemporal variables that influence transition probabilities at every time step. Particle filters fail for models that include such variables, while methods that use Gibbs sampling of parameter variables may incur a per-sample cost that grows linearly with the length of the observation sequence. Storvik devised a method for incremental computation of exact sufficient statistics that, for some cases, reduces the per-sample cost to a constant. In this paper, we demonstrate a connection between Storvik's filter and a Kalman filter in parameter space and establish more general conditions under which Storvik's filter works. Drawing on an analogy to the extended Kalman filter, we develop and analyze, both theoretically and experimentally, a Taylor approximation to the parameter posterior that allows Storvik's method to be applied to a broader class of models. Our experiments on both synthetic examples and real applications show improvement over existing methods.
BibTeX:
@inproceedings{erol2013extended,
  author = {Erol, Yusuf and Li, Lei and Ramsundar, Bharath and Russell, Stuart J.},
  title = {The Extended Parameter Filter},
  booktitle = {Proceedings of the 30th International Conference on Machine learning},
  year = {2013},
  note = {The full version appeared as Tech. Rep. UCB/EECS-2013-48.}
}
Bin Fu, Jialiu Lin, Lei Li, Christos Faloutsos, Jason Hong and Norman Sadeh (2013), "Why People Hate Your App - Making Sense of User Feedback in a Mobile App Store", In KDD '13: Proceeding of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA. ACM.
Abstract: User review is a crucial component of open mobile app mar- kets such as the Google Play Store. How do we automatically summarize millions of user reviews and make sense out of them? Unfortunately, beyond simple summaries such as histograms of user ratings, there are few analytic tools that can provide insights into user reviews. In this paper, we propose WisCom, a system that can analyze tens of millions user ratings and comments in mobile app markets at three different levels of detail. Our system is able to (a) discover inconsistencies in reviews; (b) identify reasons why users like or dislike a given app, and provide an interactive, zoomable view of how users’ reviews evolve over time; and (c) provide valuable insights into the entire app market, identifying users’ major concerns and preferences of different types of apps. Results using our techniques are reported on a 32GB dataset consisting of over 13 million user reviews of 171,493 Android apps in the Google Play Store. We discuss how the techniques presented herein can be deployed to help a mobile app market operator such as Google as well as individual app developers and end-users.
BibTeX:
@inproceedings{fu2013why,
  author = {Bin Fu and Jialiu Lin and Lei Li and Christos Faloutsos and Jason Hong and Norman Sadeh},
  title = {Why People Hate Your App - Making Sense of User Feedback in a Mobile App Store},
  booktitle = {KDD '13: Proceeding of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  publisher = {ACM},
  year = {2013}
}
Lei Li and Stuart J. Russell (2013), "The BLOG Language Reference". EECS Department, University of California, Berkeley, Technical Report UCB/EECS-2013-51, May, 2013.
Abstract: This document introduces the syntax of BLOG, a probabilistic programming language, for describing random variables and their probabilistic dependencies. BLOG defines probabilistic generative models over first-order structures. For example, all Bayesian networks can be easily described by BLOG. BLOG has the following features: (a) it employs open-universe semantics; (b) it can describe relational uncertainty; (c) it can handle identity uncertainty; and (d) it is empowered by first-order logic. The syntax as described in this document corresponds to BLOG version 0.6. The current version represents a significant redesign and extension to previous versions of BLOG, based on the principles of usability and implementation efficiency.
BibTeX:
@techreport{li2013blog,
  author = {Li, Lei and Russell, Stuart J.},
  title = {The BLOG Language Reference},
  year = {2013},
  number = {UCB/EECS-2013-51},
  url = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-51.html}
}
Lei Li, Bharath Ramsundar and Stuart Russell (2013), "Dynamic Scaled Sampling for Deterministic Constraints", In 16th International Conference on Artificial Intelligence and Statistics.
Abstract: Deterministic and near-deterministic relationships among subsets of random variables in multivariate systems are known to cause serious problems for Monte Carlo algorithms. We examine the case in which the relationship Z = f(X1,...,Xk) holds, where each Xi has a continuous prior pdf and we wish to obtain samples from the conditional distribution P(X1,...,Xk | Z = s). When f is addition, the problem is NP-hard even when the Xi are independent. In more restricted cases—for example, i.i.d. Boolean or categorical Xi—efficient exact samplers have been obtained previously. For the general continuous case, we propose a dynamic scaling algorithm (DYSC), and prove that it has O(k) expected running time and finite variance. We discuss generalizations of DYSC to functions f described by binary operation trees. We evaluate the algorithm on several examples.
BibTeX:
@inproceedings{li2013dynamic,
  author = {Lei Li and Bharath Ramsundar and Stuart Russell},
  title = {Dynamic Scaled Sampling for Deterministic Constraints},
  booktitle = {16th International Conference on Artificial Intelligence and Statistics},
  year = {2013}
}
Siyuan Liu, Lei Li and Ramayya Krishnan (2013), "Hibernating Process: Modelling Mobile Calls at Multiple Scales", In IEEE International Conference on Data Mining.
BibTeX:
@inproceedings{liu2013hibernating,
  author = {Siyuan Liu and Lei Li and Ramayya Krishnan},
  title = {Hibernating Process: Modelling Mobile Calls at Multiple Scales},
  booktitle = {IEEE International Conference on Data Mining},
  year = {2013}
}
Yasuko Matsubara, Lei Li, Evangelos E. Papalexakis, David Lo, Yasushi Sakurai and Christos Faloutsos (2013), "F-Trail: Finding Patterns in Taxi Trajectories", In The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)., pp. 86-98.
BibTeX:
@inproceedings{matsubara2013f,
  author = {Yasuko Matsubara and Lei Li and Evangelos E. Papalexakis and David Lo and Yasushi Sakurai and Christos Faloutsos},
  title = {F-Trail: Finding Patterns in Taxi Trajectories},
  booktitle = {The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)},
  year = {2013},
  pages = {86-98}
}
Mark Rogers, Lei Li and Stuart J. Russell (2013), "Multilinear Dynamical Systems for Tensor Time Series", In Advances in Neural Information Processing Systems 26.
BibTeX:
@inproceedings{rogers2013multilinear,
  author = {Mark Rogers and Lei Li and Stuart J. Russell},
  title = {Multilinear Dynamical Systems for Tensor Time Series},
  booktitle = {Advances in Neural Information Processing Systems 26},
  year = {2013},
  url = {mlds/index.html}
}
Sharad Vikram, Lei Li and Stuart Russell (2013), "Handwriting and Gestures in the Air, Recognizing on the Fly", In ACM Conference on Human Factors in Computing Systems (CHI) Extended Abstracts.
Abstract: Recent technologies in vision sensors are capable of capturing 3D finger positions and movements. We propose a novel way to control and interact with computers by moving fingers in the air. The positions of fingers are precisely captured by a computer vision device. By tracking the moving patterns of fingers, we can then recognize users’ intended control commands or input information. We demonstrate this human input approach through an example application of handwriting recognition. By treating the input as a time series of 3D positions, we propose a fast algorithm using dynamic time warping to recognize characters in online fashion. We employ various optimization techniques to recognize in real time as one writes. Experiments show promising recognition performance and speed.
BibTeX:
@inproceedings{vikram2013handwriting,
  author = {Sharad Vikram and Lei Li and Stuart Russell},
  title = {Handwriting and Gestures in the Air, Recognizing on the Fly},
  booktitle = {ACM Conference on Human Factors in Computing Systems (CHI) Extended Abstracts},
  year = {2013}
}
Keith Henderson, Brian Gallagher, Tina Eliassi-Rad, Hanghang Tong, Sugato Basu, Leman Akoglu, Danai Koutra, Christos Faloutsos and Lei Li (2012), "RolX: Structural Role Extraction and Mining in Large Graphs", In KDD '12: Proceeding of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA. ACM.
BibTeX:
@inproceedings{henderson2012rolx,
  author = {Keith Henderson and Brian Gallagher and Tina Eliassi-Rad and Hanghang Tong and Sugato Basu and Leman Akoglu and Danai Koutra and Christos Faloutsos and Lei Li},
  title = {RolX: Structural Role Extraction and Mining in Large Graphs},
  booktitle = {KDD '12: Proceeding of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  publisher = {ACM},
  year = {2012}
}
Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li and Christos Faloutsos (2012), "Rise and Fall Patterns of Information Diffusion: Model and Implications", In KDD '12: Proceeding of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA. ACM.
BibTeX:
@inproceedings{matsubara2012rise,
  author = {Yasuko Matsubara and Yasushi Sakurai and B. Aditya Prakash and Lei Li and Christos Faloutsos},
  title = {Rise and Fall Patterns of Information Diffusion: Model and Implications},
  booktitle = {KDD '12: Proceeding of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  publisher = {ACM},
  year = {2012}
}
Keith Henderson, Brian Gallagher, Lei Li, Leman Akoglu, Tina Eliassi-Rad, Hanghang Tong and Christos Faloutsos (2011), "It's Who You Know: Graph Mining Using Recursive Structural Features", In KDD '11: Proceeding of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA. ACM.
BibTeX:
@inproceedings{henderson2011its,
  author = {Keith Henderson and Brian Gallagher and Lei Li and Leman Akoglu and Tina Eliassi-Rad and Hanghang Tong and Christos Faloutsos},
  title = {It's Who You Know: Graph Mining Using Recursive Structural Features},
  booktitle = {KDD '11: Proceeding of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  publisher = {ACM},
  year = {2011}
}
Lei Li (2011), "Fast algorithms for mining co-evolving time series" . Ph.D. Dissertation, Carnegie Mellon University. , Available as technical report CMU-CS-11-127.
BibTeX:
@phdthesis{li2011fast,
  author = {Lei Li},
  title = {Fast algorithms for mining co-evolving time series},
  school = {Carnegie Mellon University},
  year = {2011}
}
Lei Li, Chieh-Jan Mike Liang, Jie Liu, Suman Nath, Andreas Terzis and Christos Faloutsos (2011), "ThermoCast: A Cyber-Physical Forecasting Model for Data Centers", In KDD '11: Proceeding of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA. ACM.
Abstract: Efficient thermal management is important in modern data centers as cooling consumes up to 50% of the total energy. Unlike previous work, we consider proactive thermal management, whereby servers can predict potential overheating events due to dynamics in data center configuration and workload, giving operators enough time to react. However, such forecasting is very challenging due to data center scales and complexity. Moreover, such a physical system is influenced by cyber effects, including workload scheduling in servers. We propose ThermoCast, a novel thermal forecasting model to predict the temperatures surrounding the servers in a data center, based on continuous streams of temperature and airflow measurements. Our approach is (a) capable of capturing cyber- physical interactions and automatically learning them from data; (b) computationally and physically scalable to data center scales; (c) able to provide online prediction with real-time sensor mea- surements. The paper’s main contributions are: (i) We provide a systematic approach to integrate physical laws and sensor observa- tions in a data center; (ii) We provide an algorithm that uses sensor data to learn the parameters of a data center’s cyber-physical sys- tem. In turn, this ability enables us to reduce model complexity compared to full-fledged fluid dynamics models, while maintain- ing forecast accuracy; (iii) Unlike previous simulation-based stud- ies, we perform experiments in a production data center. Using real data traces, we show that ThermoCast forecasts temperature 2× better than a machine learning approach solely driven by data, and can successfully predict thermal alarms 4.2 minutes ahead of time.
BibTeX:
@inproceedings{li2011thermocast,
  author = {Lei Li and Chieh-Jan Mike Liang and Jie Liu and Suman Nath and Andreas Terzis and Christos Faloutsos},
  title = {ThermoCast: A Cyber-Physical Forecasting Model for Data Centers},
  booktitle = {KDD '11: Proceeding of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  publisher = {ACM},
  year = {2011}
}
Lei Li and B. Aditya Prakash (2011), "Time Series Clustering: Complex is Simpler!", In Proceedings of the 28th International Conference on Machine learning.

Please see for updated and additional experiments in Chap 5 of the thesis "Fast algorithms for mining co-evolving time series".

BibTeX:
@inproceedings{li2011time,
  author = {Lei Li and B. Aditya Prakash},
  title = {Time Series Clustering: Complex is Simpler!},
  booktitle = {Proceedings of the 28th International Conference on Machine learning},
  year = {2011},
  note = {Please see for updated and additional experiments in Chap 5 of the thesis "Fast algorithms for mining co-evolving time series".}
}
Siyuan Liu, Lei Li, Christos Faloutsos and Lionel Ni (2011), "Mobile Phone Graph Evolution: Findings, Model and Interpretation", In IEEE International Conference on Data Mining, workshop on Data Mining Technologies for Computational Collective Intelligence.
BibTeX:
@inproceedings{liu2011mobile,
  author = {Siyuan Liu and Lei Li and Christos Faloutsos and Lionel Ni},
  title = {Mobile Phone Graph Evolution: Findings, Model and Interpretation},
  booktitle = {IEEE International Conference on Data Mining, workshop on Data Mining Technologies for Computational Collective Intelligence},
  year = {2011}
}
Yasushi Sakurai, Lei Li, Yasuko Matsubara and Christos Faloutsos (2011), "WindMine: Fast and Effective Mining of Web-click Sequences", In SIAM International Conference on Data Mining.
BibTeX:
@inproceedings{sakurai2011windmine,
  author = {Yasushi Sakurai and Lei Li and Yasuko Matsubara and Christos Faloutsos},
  title = {WindMine: Fast and Effective Mining of Web-click Sequences},
  booktitle = {SIAM International Conference on Data Mining},
  year = {2011}
}
Keith Henderson, Tina Eliassi-Rad, Christos Faloutsos, Leman Akoglu, Lei Li, Koji Maruhashi, B. Aditya Prakash and Hanghang Tong (2010), "Metric forensics: a multi-level approach for mining volatile graphs", In KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA., pp. 163-172. ACM.
BibTeX:
@inproceedings{henderson2010metric,
  author = {Henderson, Keith and Eliassi-Rad, Tina and Faloutsos, Christos and Akoglu, Leman and Li, Lei and Maruhashi, Koji and Prakash, B. Aditya and Tong, Hanghang},
  title = {Metric forensics: a multi-level approach for mining volatile graphs},
  booktitle = {KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining},
  publisher = {ACM},
  year = {2010},
  pages = {163--172},
  doi = {http://doi.acm.org/10.1145/1835804.1835828}
}
Lei Li, James McCann, Nancy Pollard and Christos Faloutsos (2010), "BoLeRO: a principled technique for including bone length constraints in motion capture occlusion filling", In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation. Aire-la-Ville, Switzerland, Switzerland., pp. 179-188. Eurographics Association.
BibTeX:
@inproceedings{li2010bolero,
  author = {Li, Lei and McCann, James and Pollard, Nancy and Faloutsos, Christos},
  title = {BoLeRO: a principled technique for including bone length constraints in motion capture occlusion filling},
  booktitle = {Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation},
  publisher = {Eurographics Association},
  year = {2010},
  pages = {179--188}
}
Lei Li, Bin Fu and Christos Faloutsos (2010), "Efficient Parallel Learning of Hidden Markov Chain Models on SMPs", IEICE Transactions on Information and Systems. Volume E93.D(6), pp. 1330-1342.

This one is applying the idea from Cut-And-Stitch paper from linear dynamical system to hidden markov models. The extended version can be found in my thesis Chapter 6 and 7.

Abstract: Quad-core cpus have been a common desktop configuration for today’s office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general par- allel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We imple- ment CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.
BibTeX:
@article{li2010efficient,
  author = {Lei Li and Bin Fu and Christos Faloutsos},
  title = {Efficient Parallel Learning of Hidden Markov Chain Models on SMPs},
  journal = {IEICE Transactions on Information and Systems},
  year = {2010},
  volume = {E93.D},
  number = {6},
  pages = {1330-1342},
  note = {This one is applying the idea from Cut-And-Stitch paper from linear dynamical system to hidden markov models. The extended version can be found in my thesis Chapter 6 and 7.}
}
Lei Li (2010), "Fast Algorithms for Time Series Mining", In 26th IEEE International Conference on Data Engineering, PHD Workshop., pp. 341-344.
BibTeX:
@inproceedings{li2010fast,
  author = {Lei Li},
  title = {Fast Algorithms for Time Series Mining},
  booktitle = {26th IEEE International Conference on Data Engineering, PHD Workshop},
  year = {2010},
  pages = {341-344}
}
Lei Li, B. Aditya Prakash and Christos Faloutsos (2010), "Parsimonious linear fingerprinting for time series", The Proceedings of the Very Large Data Bases Endowment (VLDB). September 2010. Volume 3, pp. 385-396. VLDB Endowment.
BibTeX:
@article{li2010parsimonious,
  author = {Li, Lei and Prakash, B. Aditya and Faloutsos, Christos},
  title = {Parsimonious linear fingerprinting for time series},
  journal = {The Proceedings of the Very Large Data Bases Endowment (VLDB)},
  publisher = {VLDB Endowment},
  year = {2010},
  volume = {3},
  pages = {385--396}
}
Fan Guo, Lei Li and Christos Faloutsos (2009), "Tailoring click models to user goals", In Proceedings of the 2009 workshop on Web Search Click Data. New York, NY, USA., pp. 88-92. ACM.
BibTeX:
@inproceedings{guo2009tailoring,
  author = {Guo, Fan and Li, Lei and Faloutsos, Christos},
  title = {Tailoring click models to user goals},
  booktitle = {Proceedings of the 2009 workshop on Web Search Click Data},
  publisher = {ACM},
  year = {2009},
  pages = {88--92},
  doi = {http://doi.acm.org/10.1145/1507509.1507523}
}
Lei Li, James McCann, Nancy Pollard and Christos Faloutsos (2009), "DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values", In KDD '09: Proceeding of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA. ACM.
BibTeX:
@inproceedings{li2009dynammo,
  author = {Lei Li and James McCann and Nancy Pollard and Christos Faloutsos},
  title = {DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values},
  booktitle = {KDD '09: Proceeding of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
  publisher = {ACM},
  year = {2009}
}
Fan Guo, Lei Li, Christos Faloutsos and Eric P. Xing (2008), "C-DEM: a multi-modal query system for Drosophila Embryo databases", The Proceedings of the Very Large Data Bases Endowment (VLDB). August 2008. Volume 1, pp. 1508-1511. VLDB Endowment.
BibTeX:
@article{guo2008c,
  author = {Guo, Fan and Li, Lei and Faloutsos, Christos and Xing, Eric P.},
  title = {C-DEM: a multi-modal query system for Drosophila Embryo databases},
  journal = {The Proceedings of the Very Large Data Bases Endowment (VLDB)},
  publisher = {VLDB Endowment},
  year = {2008},
  volume = {1},
  pages = {1508--1511}
}
Lei Li, Wenjie Fu, Fan Guo, Todd C. Mowry and Christos Faloutsos (2008), "Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps", In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA., pp. 471-479. ACM.
Abstract: Multi-core processors with ever increasing number of cores per chip are becoming prevalent in modern parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up data mining algorithms. Specifically, we present a parallel algorithm for approximate learning of Linear Dynamical Systems (LDS), also known as Kalman Filters (KF). LDSs are widely used in time series analysis such as motion capture modeling and visual tracking etc. We propose Cut-And-Stitch (CAS), a novel method to handle the data dependencies due to the chain structure of hidden variables in LDS, so as to parallelize the EM- based parameter learning algorithm. We implement the algorithm using OpenMP on both a supercomputer and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the serial version. In addition, Cut-And-Stitch can be generalized to other models with similar linear structures such as Hidden Markov Models (HMM) and Switching Kalman Filters (SKF).
BibTeX:
@inproceedings{li2008cut,
  author = {Li,, Lei and Fu,, Wenjie and Guo,, Fan and Mowry,, Todd C. and Faloutsos,, Christos},
  title = {Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps},
  booktitle = {KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining},
  publisher = {ACM},
  year = {2008},
  pages = {471--479}
}
Lei Li, James McCann, Christos Faloutsos and Nancy Pollard (2008), "Laziness is a virtue: Motion stitching using effort minimization", In The 29th Annual Conference of the European Association for Computer Graphics, Short Paper Proceedings.
BibTeX:
@inproceedings{li2008laziness,
  author = {Lei Li and James McCann and Christos Faloutsos and Nancy Pollard},
  title = {Laziness is a virtue: Motion stitching using effort minimization},
  booktitle = {The 29th Annual Conference of the European Association for Computer Graphics, Short Paper Proceedings},
  year = {2008}
}
Yasushi Sakurai, Rosalynn Chong, Lei Li and Christos Faloutsos (2008), "Efficient Distribution Mining and Classification", In SIAM International Conference on Data Mining., pp. 632-643.
BibTeX:
@inproceedings{sakurai2008efficient,
  author = {Yasushi Sakurai and Rosalynn Chong and Lei Li and Christos Faloutsos},
  title = {Efficient Distribution Mining and Classification},
  booktitle = {SIAM International Conference on Data Mining},
  year = {2008},
  pages = {632-643}
}
Wanhong Xu, Xi Zhou and Lei Li (2008), "Inferring privacy information via social relations", In IEEE 24th International Conference on Data Engineering workshops., pp. 525-530.
BibTeX:
@inproceedings{xu2008inferring,
  author = {Wanhong Xu and Xi Zhou and Lei Li},
  title = {Inferring privacy information via social relations},
  booktitle = {IEEE 24th International Conference on Data Engineering workshops},
  year = {2008},
  pages = {525-530},
  doi = {http://dx.doi.org/10.1109/ICDEW.2008.4498373}
}
Lei Li, Qiaoling Liu, Yunfeng Tao, Lei Zhang, Jian Zhou and Yong Yu (2006), "Providing an Uncertainty Reasoning Service for Semantic Web Application", In Asia-Pacific Web Conference., pp. 628-639.
BibTeX:
@inproceedings{li2006providing,
  author = {Lei Li and Qiaoling Liu and Yunfeng Tao and Lei Zhang and Jian Zhou and Yong Yu},
  title = {Providing an Uncertainty Reasoning Service for Semantic Web Application},
  booktitle = {Asia-Pacific Web Conference},
  year = {2006},
  pages = {628-639}
}

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