NOMAD: Nonlocking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion
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Inderjit S. Dhillon | Cho-Jui Hsieh | S. V. N. Vishwanathan | Hsiang-Fu Yu | Hyokun Yun | Cho-Jui Hsieh | S. Vishwanathan | I. Dhillon | Hsiang-Fu Yu | Hyokun Yun
[1] Harold J. Kushner,et al. wchastic. approximation methods for constrained and unconstrained systems , 1978 .
[2] John N. Tsitsiklis,et al. Parallel and distributed computation , 1989 .
[3] Donald J. Patterson,et al. Computer organization and design: the hardware-software interface (appendix a , 1993 .
[4] 장훈,et al. [서평]「Computer Organization and Design, The Hardware/Software Interface」 , 1997 .
[5] Sanjay Ghemawat,et al. MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.
[6] Kunle Olukotun,et al. Map-Reduce for Machine Learning on Multicore , 2006, NIPS.
[7] H. Robbins. A Stochastic Approximation Method , 1951 .
[8] Torsten Hoefler,et al. Optimizing a conjugate gradient solver with non-blocking collective operations , 2007, Parallel Comput..
[9] Léon Bottou,et al. The Tradeoffs of Large Scale Learning , 2007, NIPS.
[10] Yehuda Koren,et al. Lessons from the Netflix prize challenge , 2007, SKDD.
[11] Dennis M. Wilkinson,et al. Large-Scale Parallel Collaborative Filtering for the Netflix Prize , 2008, AAIM.
[12] Nathan Srebro,et al. SVM optimization: inverse dependence on training set size , 2008, ICML '08.
[13] John Langford,et al. Slow Learners are Fast , 2009, NIPS.
[14] Alexander J. Smola,et al. An architecture for parallel topic models , 2010, Proc. VLDB Endow..
[15] Joseph E. Gonzalez,et al. GraphLab: A New Parallel Framework for Machine Learning , 2010 .
[16] Alexander J. Smola,et al. Bundle Methods for Regularized Risk Minimization , 2010, J. Mach. Learn. Res..
[17] Scott Shenker,et al. Spark: Cluster Computing with Working Sets , 2010, HotCloud.
[18] Daniel B. Szyld,et al. Asynchronous Iterations , 2011, Encyclopedia of Parallel Computing.
[19] Stephen J. Wright,et al. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent , 2011, NIPS.
[20] Inderjit S. Dhillon,et al. Fast coordinate descent methods with variable selection for non-negative matrix factorization , 2011, KDD.
[21] Peter J. Haas,et al. Large-scale matrix factorization with distributed stochastic gradient descent , 2011, KDD.
[22] Sergei Vassilvitskii,et al. Counting triangles and the curse of the last reducer , 2011, WWW.
[23] Rainer Gemulla,et al. Distributed Matrix Completion , 2012, 2012 IEEE 12th International Conference on Data Mining.
[24] Carlos Guestrin,et al. Distributed GraphLab : A Framework for Machine Learning and Data Mining in the Cloud , 2012 .
[25] Yehuda Koren,et al. The Yahoo! Music Dataset and KDD-Cup '11 , 2012, KDD Cup.
[26] Inderjit S. Dhillon,et al. Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems , 2012, 2012 IEEE 12th International Conference on Data Mining.
[27] Joseph Gonzalez,et al. PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs , 2012, OSDI.
[28] Tim Kraska,et al. MLbase: A Distributed Machine-learning System , 2013, CIDR.
[29] Chih-Jen Lin,et al. A fast parallel SGD for matrix factorization in shared memory systems , 2013, RecSys.
[30] Christopher Ré,et al. Parallel stochastic gradient algorithms for large-scale matrix completion , 2013, Mathematical Programming Computation.
[31] John Langford,et al. A reliable effective terascale linear learning system , 2011, J. Mach. Learn. Res..
[32] Peter Richtárik,et al. Distributed Coordinate Descent Method for Learning with Big Data , 2013, J. Mach. Learn. Res..