Training Gaussian Mixture Models at Scale via Coresets
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Andreas Krause | Matthew Faulkner | Dan Feldman | Mario Lucic | Andreas Krause | Mario Lucic | Dan Feldman | Matthew Faulkner
[1] Sanjeev Arora,et al. LEARNING MIXTURES OF SEPARATED NONSPHERICAL GAUSSIANS , 2005, math/0503457.
[2] Anima Anandkumar,et al. Tensor decompositions for learning latent variable models , 2012, J. Mach. Learn. Res..
[3] Jeff M. Phillips,et al. Coresets and Sketches , 2016, ArXiv.
[4] Tamir Tassa,et al. More Constraints, Smaller Coresets: Constrained Matrix Approximation of Sparse Big Data , 2015, KDD.
[5] Artem Barger,et al. k-Means for Streaming and Distributed Big Sparse Data , 2015, SDM.
[6] Mikhail Belkin,et al. Polynomial Learning of Distribution Families , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[7] Alexander J. Smola,et al. Communication Efficient Coresets for Empirical Loss Minimization , 2015, UAI.
[8] L. Schulman,et al. Universal ε-approximators for integrals , 2010, SODA '10.
[9] Santosh S. Vempala,et al. A spectral algorithm for learning mixture models , 2004, J. Comput. Syst. Sci..
[10] Kasturi R. Varadarajan,et al. Geometric Approximation via Coresets , 2007 .
[11] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[12] Petros Drineas,et al. CUR matrix decompositions for improved data analysis , 2009, Proceedings of the National Academy of Sciences.
[13] Dan Feldman,et al. The single pixel GPS: learning big data signals from tiny coresets , 2012, SIGSPATIAL/GIS.
[14] Dan Feldman,et al. Turning big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering , 2013, SODA.
[15] Michael Langberg,et al. A unified framework for approximating and clustering data , 2011, STOC '11.
[16] John W. Fisher,et al. Coresets for k-Segmentation of Streaming Data , 2014, NIPS.
[17] Andreas Krause,et al. The next big one: Detecting earthquakes and other rare events from community-based sensors , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.
[18] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[19] Artur Czumaj,et al. Sublinear‐time approximation algorithms for clustering via random sampling , 2007, Random Struct. Algorithms.
[20] Dan Feldman. Coresets for Weighted Facilities and Their Applications , 2006 .
[21] Dan Feldman,et al. Learning Big (Image) Data via Coresets for Dictionaries , 2013, Journal of Mathematical Imaging and Vision.
[22] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[23] Jon Feldman,et al. PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption , 2006, COLT.
[24] Andreas Krause,et al. Linear-Time Outlier Detection via Sensitivity , 2016, IJCAI.
[25] P. Baldi,et al. Searching for exotic particles in high-energy physics with deep learning , 2014, Nature Communications.
[26] Andreas Krause,et al. Fast and Provably Good Seedings for k-Means , 2016, NIPS.
[27] Ankit Aggarwal,et al. Adaptive Sampling for k-Means Clustering , 2009, APPROX-RANDOM.
[28] Ankur Moitra,et al. Settling the Polynomial Learnability of Mixtures of Gaussians , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[29] Sariel Har-Peled,et al. High-Dimensional Shape Fitting in Linear Time , 2003, SCG '03.
[30] Andreas Krause,et al. Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures , 2015, AISTATS.
[31] Sariel Har-Peled,et al. On coresets for k-means and k-median clustering , 2004, STOC '04.
[32] Anima Anandkumar,et al. A Method of Moments for Mixture Models and Hidden Markov Models , 2012, COLT.
[33] Andreas Krause,et al. Approximate K-Means++ in Sublinear Time , 2016, AAAI.
[34] Sanjay Ghemawat,et al. MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.
[35] Sergei Vassilvitskii,et al. Scalable K-Means++ , 2012, Proc. VLDB Endow..
[36] Andreas Krause,et al. Scalable Training of Mixture Models via Coresets , 2011, NIPS.
[37] Amos Fiat,et al. Coresets forWeighted Facilities and Their Applications , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).
[38] Dan Feldman,et al. A PTAS for k-means clustering based on weak coresets , 2007, SCG '07.
[39] Andreas Krause,et al. Coresets for Nonparametric Estimation - the Case of DP-Means , 2015, ICML.
[40] Maxim Sviridenko,et al. A Bi-Criteria Approximation Algorithm for k-Means , 2015, APPROX-RANDOM.
[41] Vladimir Braverman,et al. New Frameworks for Offline and Streaming Coreset Constructions , 2016, ArXiv.
[42] Christian Sohler,et al. Coresets in dynamic geometric data streams , 2005, STOC '05.
[43] Andreas Krause,et al. Practical Coreset Constructions for Machine Learning , 2017, 1703.06476.
[44] Jon Louis Bentley,et al. Decomposable Searching Problems I: Static-to-Dynamic Transformation , 1980, J. Algorithms.
[45] Andreas Krause,et al. Scalable and Distributed Clustering via Lightweight Coresets , 2017, ArXiv.
[46] Sanjoy Dasgupta,et al. Learning mixtures of Gaussians , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).
[47] David Haussler,et al. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..
[48] Peter L. Bartlett,et al. Neural Network Learning - Theoretical Foundations , 1999 .
[49] Yohji Akama,et al. VC dimension of ellipsoids , 2011, ArXiv.
[50] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[51] Michael D. Vose,et al. A Linear Algorithm For Generating Random Numbers With a Given Distribution , 1991, IEEE Trans. Software Eng..
[52] Sanjoy Dasgupta,et al. A Two-Round Variant of EM for Gaussian Mixtures , 2000, UAI.
[53] Andreas Krause,et al. Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning , 2015, AISTATS.
[54] Yingyu Liang,et al. Distributed k-Means and k-Median Clustering on General Topologies , 2013, NIPS 2013.