Algorithms for Massive Data Problems: Streaming, Sketching, and Sampling
暂无分享,去创建一个
[1] Kun He,et al. Hidden Community Detection in Social Networks , 2017, Inf. Sci..
[2] Armin Biere,et al. SAT Race 2015 , 2016, Artif. Intell..
[3] Sanjeev Arora,et al. Computing a Nonnegative Matrix Factorization - Provably , 2016, SIAM J. Comput..
[4] Fionn Murtagh,et al. Handbook of Cluster Analysis , 2015 .
[5] Kun He,et al. Detecting Overlapping Communities from Local Spectral Subspaces , 2015, 2015 IEEE International Conference on Data Mining.
[6] Kun He,et al. Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach , 2015, WWW.
[7] Kun He,et al. Revealing Multiple Layers of Hidden Community Structure in Networks , 2015, ArXiv.
[8] Chiranjib Bhattacharyya,et al. A provable SVD-based algorithm for learning topics in dominant admixture corpus , 2014, NIPS.
[9] Maria-Florina Balcan,et al. Robust hierarchical clustering , 2013, J. Mach. Learn. Res..
[10] Maria-Florina Balcan,et al. Clustering under approximation stability , 2013, JACM.
[11] Sanjeev Arora,et al. The Multiplicative Weights Update Method: a Meta-Algorithm and Applications , 2012, Theory Comput..
[12] David B. Dunson,et al. Probabilistic topic models , 2011, KDD '11 Tutorials.
[13] Benny Sudakov,et al. The phase transition in random graphs: A simple proof , 2012, Random Struct. Algorithms.
[14] Mark Braverman,et al. Finding Endogenously Formed Communities , 2012, SODA.
[15] Amin Coja-Oghlan,et al. On the solution‐space geometry of random constraint satisfaction problems , 2011, Random Struct. Algorithms.
[16] Francis R. Bach,et al. Online Learning for Latent Dirichlet Allocation , 2010, NIPS.
[17] Allan Sly,et al. Computational Transition at the Uniqueness Threshold , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[18] Ankur Moitra,et al. Settling the Polynomial Learnability of Mixtures of Gaussians , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[19] Maria-Florina Balcan,et al. A discriminative model for semi-supervised learning , 2010, J. ACM.
[20] Adam N. Elmachtoub,et al. From Random Polygon to Ellipse: An Eigenanalysis , 2010, SIAM Rev..
[21] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[22] Sanjoy Dasgupta,et al. Two faces of active learning , 2011, Theor. Comput. Sci..
[23] Santosh S. Vempala,et al. Spectral Algorithms , 2009, Found. Trends Theor. Comput. Sci..
[24] Robert D. Nowak,et al. Multi-Manifold Semi-Supervised Learning , 2009, AISTATS.
[25] Anil K. Jain. Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..
[26] Ravi Kannan,et al. A New Probability Inequality Using Typical Moments and Concentration Results , 2008, 2009 50th Annual IEEE Symposium on Foundations of Computer Science.
[27] N. Alon,et al. The Probabilistic Method: Alon/Probabilistic , 2008 .
[28] Santosh S. Vempala,et al. A discriminative framework for clustering via similarity functions , 2008, STOC.
[29] M. Bayati,et al. Max-Product for Maximum Weight Matching: Convergence, Correctness, and LP Duality , 2008, IEEE Transactions on Information Theory.
[30] Vahab S. Mirrokni,et al. Local Computation of PageRank Contributions , 2007, Internet Math..
[31] Pablo A. Parrilo,et al. Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..
[32] Sanjoy Dasgupta,et al. A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians , 2007, J. Mach. Learn. Res..
[33] Delbert Dueck,et al. Clustering by Passing Messages Between Data Points , 2007, Science.
[34] P. Balister,et al. BRANCHING PROCESSES , 2006 .
[35] U. Helmke,et al. A Newton-like method for solving rank constrained linear matrix inequalities , 2006, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).
[36] Petros Drineas,et al. Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication , 2006, SIAM J. Comput..
[37] Petros Drineas,et al. FAST MONTE CARLO ALGORITHMS FOR MATRICES II: COMPUTING A LOW-RANK APPROXIMATION TO A MATRIX∗ , 2004 .
[38] Maria-Florina Balcan,et al. Agnostic active learning , 2006, J. Comput. Syst. Sci..
[39] Jon M. Kleinberg,et al. On learning mixtures of heavy-tailed distributions , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).
[40] Devavrat Shah,et al. Maximum weight matching via max-product belief propagation , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..
[41] W. Freeman,et al. Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.
[42] Yishay Mansour,et al. From External to Internal Regret , 2005, J. Mach. Learn. Res..
[43] Dimitris Achlioptas,et al. On Spectral Learning of Mixtures of Distributions , 2005, COLT.
[44] Santosh S. Vempala,et al. The Random Projection Method , 2005, DIMACS Series in Discrete Mathematics and Theoretical Computer Science.
[45] Sanjeev Arora,et al. LEARNING MIXTURES OF SEPARATED NONSPHERICAL GAUSSIANS , 2005, math/0503457.
[46] Victoria Stodden,et al. When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts? , 2003, NIPS.
[47] Rémi Gribonval,et al. Sparse decompositions in "incoherent" dictionaries , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
[48] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[49] Y. Azar,et al. Optimal oblivious routing in polynomial time , 2003, STOC '03.
[50] Yuval Peres,et al. The threshold for random k-SAT is 2k (ln 2 - O(k)) , 2003, STOC '03.
[51] Peter L. Bartlett,et al. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..
[52] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[53] Riccardo Zecchina,et al. Survey propagation: An algorithm for satisfiability , 2002, Random Struct. Algorithms.
[54] D. Spielman. The Smoothed Analysis of Algorithms , 2002, FCT.
[55] Nello Cristianini,et al. Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..
[56] M. Mézard,et al. Analytic and Algorithmic Solution of Random Satisfiability Problems , 2002, Science.
[57] Frank McSherry,et al. Spectral partitioning of random graphs , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.
[58] Shang-Hua Teng,et al. Smoothed analysis of algorithms: why the simplex algorithm usually takes polynomial time , 2001, STOC '01.
[59] Zoubin Ghahramani,et al. An Introduction to Hidden Markov Models and Bayesian Networks , 2001, Int. J. Pattern Recognit. Artif. Intell..
[60] J. Hopcroft,et al. Are randomly grown graphs really random? , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[61] B. Frey,et al. Factor graphs and the sum-product algorithm , 2001, IEEE Trans. Inf. Theory.
[62] William T. Freeman,et al. On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs , 2001, IEEE Trans. Inf. Theory.
[63] Santosh S. Vempala,et al. On clusterings-good, bad and spectral , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.
[64] Jon M. Kleinberg,et al. The small-world phenomenon: an algorithmic perspective , 2000, STOC '00.
[65] Jon Kleinberg,et al. Authoritative sources in a hyperlinked environment , 1999, SODA '98.
[66] Thorsten Joachims,et al. Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.
[67] E. Friedgut,et al. Sharp thresholds of graph properties, and the -sat problem , 1999 .
[68] Alan M. Frieze,et al. Quick Approximation to Matrices and Applications , 1999, Comb..
[69] Alan M. Frieze,et al. Fast Monte-Carlo algorithms for finding low-rank approximations , 1998, Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No.98CB36280).
[70] John Shawe-Taylor,et al. Structural Risk Minimization Over Data-Dependent Hierarchies , 1998, IEEE Trans. Inf. Theory.
[71] Duncan J. Watts,et al. Collective dynamics of ‘small-world’ networks , 1998, Nature.
[72] Geoffrey Zweig,et al. Syntactic Clustering of the Web , 1997, Comput. Networks.
[73] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[74] Manfred K. Warmuth,et al. How to use expert advice , 1997, JACM.
[75] Alan M. Frieze,et al. Analysis of Two Simple Heuristics on a Random Instance of k-SAT , 1996, J. Algorithms.
[76] David P. Williamson,et al. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming , 1995, JACM.
[77] Bruce A. Reed,et al. A Critical Point for Random Graphs with a Given Degree Sequence , 1995, Random Struct. Algorithms.
[78] S. Janson,et al. The Birth of the Giant Component , 1993, Random Struct. Algorithms.
[79] Sebastian Thrun,et al. Lifelong robot learning , 1993, Robotics Auton. Syst..
[80] Bruce A. Reed,et al. Mick gets some (the odds are on his side) (satisfiability) , 1992, Proceedings., 33rd Annual Symposium on Foundations of Computer Science.
[81] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[82] Ingrid Daubechies,et al. The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.
[83] Richard M. Karp,et al. The Transitive Closure of a Random Digraph , 1990, Random Struct. Algorithms.
[84] Manfred K. Warmuth,et al. The weighted majority algorithm , 1989, 30th Annual Symposium on Foundations of Computer Science.
[85] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.
[86] Mark Jerrum,et al. Approximate Counting, Uniform Generation and Rapidly Mixing Markov Chains , 1987, WG.
[87] David Haussler,et al. Occam's Razor , 1987, Inf. Process. Lett..
[88] Ming-Te Chao,et al. Probabilistic Analysis of Two Heuristics for the 3-Satisfiability Problem , 1986, SIAM J. Comput..
[89] N. Alon. Eigenvalues and expanders , 1986, Comb..
[90] Philippe Flajolet,et al. Probabilistic Counting Algorithms for Data Base Applications , 1985, J. Comput. Syst. Sci..
[91] Béla Bollobás,et al. Random Graphs, Second Edition , 2001, Cambridge Studies in Advanced Mathematics.
[92] L. Valiant,et al. A theory of the learnable , 1984, CACM.
[93] Jayadev Misra,et al. Finding Repeated Elements , 1982, Sci. Comput. Program..
[94] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[95] B. Parlett. The Symmetric Eigenvalue Problem , 1981 .
[96] Gerard Salton,et al. A vector space model for automatic indexing , 1975, CACM.
[97] M. Satterthwaite. Strategy-proofness and Arrow's conditions: Existence and correspondence theorems for voting procedures and social welfare functions , 1975 .
[98] A. Gibbard. Manipulation of Voting Schemes: A General Result , 1973 .
[99] J. H. Ward. Hierarchical Grouping to Optimize an Objective Function , 1963 .
[100] Maria-Florina Balcan,et al. Active Learning – Modern Learning Theory , 2015 .
[101] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2009 .
[102] Francesco Masulli,et al. A survey of kernel and spectral methods for clustering , 2008, Pattern Recognit..
[103] Henry A. Kautz,et al. Satisfiability Solvers , 2008, Handbook of Knowledge Representation.
[104] Yoshua Bengio. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[105] William T. Freeman,et al. Understanding belief propagation and its generalizations , 2003 .
[106] R. Motwani,et al. Chapter 31 – Approximate Frequency Counts over Data Streams , 2002, VLDB 2002.
[107] Jon M. Kleinberg,et al. An Impossibility Theorem for Clustering , 2002, NIPS.
[108] Alan M. Frieze,et al. Clustering in large graphs and matrices , 1999, SODA '99.
[109] A. Andrew,et al. Emergence of Scaling in Random Networks , 1999 .
[110] Anupam Gupta,et al. An elementary proof of the Johnson-Lindenstrauss Lemma , 1999 .
[111] Rajeev Motwani,et al. What can you do with a Web in your Pocket? , 1998, IEEE Data Eng. Bull..
[112] Béla Bollobás,et al. Threshold functions , 1987, Comb..
[113] Teofilo F. GONZALEZ,et al. Clustering to Minimize the Maximum Intercluster Distance , 1985, Theor. Comput. Sci..
[114] P. Erdos,et al. On the evolution of random graphs , 1984 .
[115] L. G. H. Cijan. A polynomial algorithm in linear programming , 1979 .
[116] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .
[117] N. Z. Shor. Convergence rate of the gradient descent method with dilatation of the space , 1970 .
[118] H. D. Block. The perceptron: a model for brain functioning. I , 1962 .
[119] K. Florek,et al. Sur la liaison et la division des points d'un ensemble fini , 1951 .
[120] K. Arrow,et al. You have printed the following article : A Difficulty in the Concept of Social Welfare , 2022 .