暂无分享,去创建一个
Chong Wang | David M. Blei | Matthew D. Hoffman | John W. Paisley | D. Blei | M. Hoffman | Chong Wang | J. Paisley
[1] Wm. R. Wright. General Intelligence, Objectively Determined and Measured. , 1905 .
[2] R. A. Leibler,et al. On Information and Sufficiency , 1951 .
[3] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[4] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[5] R. E. Kalman,et al. A New Approach to Linear Filtering and Prediction Problems , 2002 .
[6] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[7] Donald B. Rubin,et al. Max-imum Likelihood from Incomplete Data , 1972 .
[8] D. Blackwell,et al. Ferguson Distributions Via Polya Urn Schemes , 1973 .
[9] T. Ferguson. A Bayesian Analysis of Some Nonparametric Problems , 1973 .
[10] C. Antoniak. Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .
[11] Empirical Bayes Methods , 1974 .
[12] Seymour Geisser,et al. The Predictive Sample Reuse Method with Applications , 1975 .
[13] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[14] S. Amari. Differential Geometry of Curved Exponential Families-Curvatures and Information Loss , 1982 .
[15] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Carsten Peterson,et al. A Mean Field Theory Learning Algorithm for Neural Networks , 1987, Complex Syst..
[17] Bernard Widrow,et al. Adaptive switching circuits , 1988 .
[18] G. Parisi,et al. Statistical Field Theory , 1988 .
[19] P. McCullagh,et al. Generalized Linear Models , 1992 .
[20] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[21] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[22] Adrian F. M. Smith,et al. Sampling-Based Approaches to Calculating Marginal Densities , 1990 .
[23] J. Sethuraman. A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS , 1991 .
[24] Jayaran Sethuramant. A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS , 1991 .
[25] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[26] Steve R. Waterhouse,et al. Bayesian Methods for Mixtures of Experts , 1995, NIPS.
[27] M. Escobar,et al. Bayesian Density Estimation and Inference Using Mixtures , 1995 .
[28] Michael I. Jordan,et al. Exploiting Tractable Substructures in Intractable Networks , 1995, NIPS.
[29] Michael I. Jordan,et al. Mean Field Theory for Sigmoid Belief Networks , 1996, J. Artif. Intell. Res..
[30] Michael I. Jordan,et al. Variational methods for inference and estimation in graphical models , 1997 .
[31] Shun-ichi Amari,et al. Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.
[32] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[33] Léon Bottou,et al. On-line learning and stochastic approximations , 1999 .
[34] Hagai Attias,et al. A Variational Bayesian Framework for Graphical Models , 1999 .
[35] Hagai Attias,et al. Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.
[36] Zoubin Ghahramani,et al. Variational Inference for Bayesian Mixtures of Factor Analysers , 1999, NIPS.
[37] Michael E. Tipping,et al. Probabilistic Principal Component Analysis , 1999 .
[38] Shin Ishii,et al. On-line EM Algorithm for the Normalized Gaussian Network , 2000, Neural Computation.
[39] Radford M. Neal. Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .
[40] Zoubin Ghahramani,et al. Propagation Algorithms for Variational Bayesian Learning , 2000, NIPS.
[41] Wim Wiegerinck,et al. Variational Approximations between Mean Field Theory and the Junction Tree Algorithm , 2000, UAI.
[42] Nando de Freitas,et al. An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.
[43] Sanjoy Dasgupta,et al. A Generalization of Principal Components Analysis to the Exponential Family , 2001, NIPS.
[44] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[45] Masa-aki Sato,et al. Online Model Selection Based on the Variational Bayes , 2001, Neural Computation.
[46] Eric R. Ziegel,et al. Generalized Linear Models , 2002, Technometrics.
[47] David J. Spiegelhalter,et al. VIBES: A Variational Inference Engine for Bayesian Networks , 2002, NIPS.
[48] Tom Minka,et al. Expectation-Propogation for the Generative Aspect Model , 2002, UAI.
[49] Matthew J. Beal. Variational algorithms for approximate Bayesian inference , 2003 .
[50] James C. Spall,et al. Introduction to stochastic search and optimization - estimation, simulation, and control , 2003, Wiley-Interscience series in discrete mathematics and optimization.
[51] Yann LeCun,et al. Large Scale Online Learning , 2003, NIPS.
[52] Michael I. Jordan,et al. A generalized mean field algorithm for variational inference in exponential families , 2002, UAI.
[53] Léon Bottou,et al. Stochastic Learning , 2003, Advanced Lectures on Machine Learning.
[54] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[55] Tim Hesterberg,et al. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control , 2004, Technometrics.
[56] Michael I. Jordan,et al. An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.
[57] Michael I. Jordan,et al. Factorial Hidden Markov Models , 1995, Machine Learning.
[58] Mark Steyvers,et al. Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[59] Yoram Singer,et al. The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.
[60] Christian P. Robert,et al. Monte Carlo Statistical Methods (Springer Texts in Statistics) , 2005 .
[61] Charles M. Bishop,et al. Variational Message Passing , 2005, J. Mach. Learn. Res..
[62] Yee Whye Teh,et al. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation , 2006, NIPS.
[63] Mark Girolami,et al. Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors , 2006, Neural Computation.
[64] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[65] John D. Lafferty,et al. Dynamic topic models , 2006, ICML.
[66] Michael I. Jordan,et al. Hierarchical Dirichlet Processes , 2006 .
[67] Michael I. Jordan,et al. Variational inference for Dirichlet process mixtures , 2006 .
[68] Yee Whye Teh,et al. Collapsed Variational Inference for HDP , 2007, NIPS.
[69] H. Robbins. A Stochastic Approximation Method , 1951 .
[70] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[71] David A. Maltz,et al. Fast Variational Inference for Large-scale Internet Diagnosis , 2007, NIPS.
[72] James C. Spall,et al. Introduction to Stochastic Search and Optimization. Estimation, Simulation, and Control (Spall, J.C. , 2007 .
[73] Juha Karhunen,et al. Natural Conjugate Gradient in Variational Inference , 2007, ICONIP.
[74] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2007, ICML '07.
[75] Chong Wang,et al. Variational Bayesian Approach to Canonical Correlation Analysis , 2007, IEEE Transactions on Neural Networks.
[76] Léon Bottou,et al. Learning using Large Datasets , 2007, NATO ASI Mining Massive Data Sets for Security.
[77] John D. Lafferty,et al. A correlated topic model of Science , 2007, 0708.3601.
[78] Michael I. Jordan,et al. An HDP-HMM for systems with state persistence , 2008, ICML '08.
[79] Chong Wang,et al. Continuous Time Dynamic Topic Models , 2008, UAI.
[80] Ruslan Salakhutdinov,et al. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo , 2008, ICML '08.
[81] Sunita Sarawagi. Learning with Graphical Models , 2008 .
[82] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[83] Yee Whye Teh,et al. On Smoothing and Inference for Topic Models , 2009, UAI.
[84] Joseph Hilbe,et al. Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .
[85] Max Welling,et al. Distributed Algorithms for Topic Models , 2009, J. Mach. Learn. Res..
[86] Lawrence Carin,et al. Nonparametric factor analysis with beta process priors , 2009, ICML '09.
[87] Dan Klein,et al. Learning Semantic Correspondences with Less Supervision , 2009, ACL.
[88] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[89] Andrew McCallum,et al. Rethinking LDA: Why Priors Matter , 2009, NIPS.
[90] O. Cappé,et al. On‐line expectation–maximization algorithm for latent data models , 2009 .
[91] Lawrence Carin,et al. Hidden Markov Models With Stick-Breaking Priors , 2009, IEEE Transactions on Signal Processing.
[92] Perry R. Cook,et al. Bayesian Nonparametric Matrix Factorization for Recorded Music , 2010, ICML.
[93] Francis R. Bach,et al. Online Learning for Latent Dirichlet Allocation , 2010, NIPS.
[94] Alexander J. Smola,et al. An architecture for parallel topic models , 2010, Proc. VLDB Endow..
[95] David M. Blei,et al. Probabilistic topic models , 2012, Commun. ACM.
[96] P. Müller,et al. Bayesian Nonparametrics: An invitation to Bayesian nonparametrics , 2010 .
[97] Guillermo Sapiro,et al. Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..
[98] Tom Minka,et al. Non-conjugate Variational Message Passing for Multinomial and Binary Regression , 2011, NIPS.
[99] Samuel J. Gershman,et al. A Tutorial on Bayesian Nonparametric Models , 2011, 1106.2697.
[100] D. Blei,et al. The Discrete Innite Logistic Normal Distribution , 2011, 1103.4789.
[101] Michael I. Jordan,et al. A Sticky HDP-HMM With Application to Speaker Diarization , 2009, 0905.2592.
[102] Chong Wang,et al. Online Variational Inference for the Hierarchical Dirichlet Process , 2011, AISTATS.
[103] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[104] Michael I. Jordan,et al. Bayesian Nonparametric Inference of Switching Dynamic Linear Models , 2010, IEEE Transactions on Signal Processing.
[105] Kristian Kersting,et al. Larger Residuals, Less Work: Active Document Scheduling for Latent Dirichlet Allocation , 2011, ECML/PKDD.
[106] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[107] David M. Blei,et al. Sparse stochastic inference for latent Dirichlet allocation , 2012, ICML.
[108] David M. Blei,et al. Nonparametric variational inference , 2012, ICML.
[109] Alexander J. Smola,et al. Scalable inference in latent variable models , 2012, WSDM '12.
[110] Michael J. Freedman,et al. Scalable Inference of Overlapping Communities , 2012, NIPS.
[111] Michael I. Jordan,et al. Variational Bayesian Inference with Stochastic Search , 2012, ICML.
[112] Chong Wang,et al. An Adaptive Learning Rate for Stochastic Variational Inference , 2013, ICML.
[113] Chong Wang,et al. Variational inference in nonconjugate models , 2012, J. Mach. Learn. Res..
[114] Chong Wang,et al. Nested Hierarchical Dirichlet Processes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.