메뉴 건너뛰기




Volumn , Issue , 2016, Pages 2378-2386

Stein variational gradient descent: A general purpose Bayesian inference algorithm

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; ITERATIVE METHODS; OPTIMIZATION;

EID: 85018878907     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (1043)

References (34)
  • 2
    • 80053452150 scopus 로고    scopus 로고
    • Bayesian learning via stochastic gradient langevin dynamics
    • M. Welling and Y. W. Teh. Bayesian learning via stochastic gradient Langevin dynamics. In ICML, 2011.
    • (2011) ICML
    • Welling, M.1    Teh, Y.W.2
  • 3
    • 84923309614 scopus 로고    scopus 로고
    • Firefly Monte Carlo: Exact MCMC with subsets of data
    • D. Maclaurin and R. P. Adams. Firefly Monte Carlo: Exact MCMC with subsets of data. In UAI, 2014.
    • (2014) UAI
    • Maclaurin, D.1    Adams, R.P.2
  • 5
    • 84867112898 scopus 로고    scopus 로고
    • Nonparametric variational inference
    • S. Gershman, M. Hoffman, and D. Blei. Nonparametric variational inference. In ICML, 2012.
    • (2012) ICML
    • Gershman, S.1    Hoffman, M.2    Blei, D.3
  • 7
    • 85047007442 scopus 로고    scopus 로고
    • Provable Bayesian inference via particle mirror descent
    • B. Dai, N. He, H. Dai, and L. Song. Provable Bayesian inference via particle mirror descent. In AISTATS, 2016.
    • (2016) AISTATS
    • Dai, B.1    He, N.2    Dai, H.3    Song, L.4
  • 11
    • 84965097708 scopus 로고    scopus 로고
    • Measuring sample quality with Stein's method
    • J. Gorham and L. Mackey. Measuring sample quality with Stein's method. In NIPS, pages 226-234, 2015.
    • (2015) NIPS , pp. 226-234
    • Gorham, J.1    Mackey, L.2
  • 13
    • 84969776493 scopus 로고    scopus 로고
    • Variational inference with normalizing flows
    • D. J. Rezende and S. Mohamed. Variational inference with normalizing flows. In ICML, 2015.
    • (2015) ICML
    • Rezende, D.J.1    Mohamed, S.2
  • 17
    • 77953218689 scopus 로고    scopus 로고
    • Random features for large-scale kernel machines
    • A. Rahimi and B. Recht. Random features for large-scale kernel machines. In NIPS, pages 1177-1184, 2007.
    • (2007) NIPS , pp. 1177-1184
    • Rahimi, A.1    Recht, B.2
  • 19
    • 84919786928 scopus 로고    scopus 로고
    • Doubly stochastic variational bayes for non-conjugate inference
    • M. Titsias and M. Lázaro-Gredilla. Doubly stochastic variational Bayes for non-conjugate inference. In ICML, pages 1971-1979, 2014.
    • (2014) ICML , pp. 1971-1979
    • Titsias, M.1    Lázaro-Gredilla, M.2
  • 20
    • 84877742737 scopus 로고    scopus 로고
    • Affine independent variational inference
    • E. Challis and D. Barber. Affine independent variational inference. In NIPS, 2012.
    • (2012) NIPS
    • Challis, E.1    Barber, D.2
  • 23
    • 84898985963 scopus 로고    scopus 로고
    • Approximating posterior distributions in belief networks using mixtures
    • C. M. B. N. Lawrence and T. J. M. I. Jordan. Approximating posterior distributions in belief networks using mixtures. In NIPS, 1998.
    • (1998) NIPS
    • Lawrence, C.M.B.N.1    Jordan, T.J.M.I.2
  • 24
    • 0001837853 scopus 로고    scopus 로고
    • Improving the mean field approximation via the use of mixture distributions
    • MIT Press
    • T. S. Jaakkola and M. I. Jordon. Improving the mean field approximation via the use of mixture distributions. In Learning in graphical models, pages 163-173. MIT Press, 1999.
    • (1999) Learning in Graphical Models , pp. 163-173
    • Jaakkola, T.S.1    Jordon, M.I.2
  • 29
    • 84901687683 scopus 로고    scopus 로고
    • The no-U-turn sampler: Adaptively setting path lengths in hamiltonian Monte Carlo
    • M. D. Hoffman and A. Gelman. The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. The Journal of Machine Learning Research, 15(1):1593-1623, 2014.
    • (2014) The Journal of Machine Learning Research , vol.15 , Issue.1 , pp. 1593-1623
    • Hoffman, M.D.1    Gelman, A.2
  • 30
    • 84969909658 scopus 로고    scopus 로고
    • Probabilistic backpropagation for scalable learning of Bayesian neural networks
    • J. M. Hernández-Lobato and R. P. Adams. Probabilistic backpropagation for scalable learning of Bayesian neural networks. In ICML, 2015.
    • (2015) ICML
    • Hernández-Lobato, J.M.1    Adams, R.P.2
  • 31
    • 84855997153 scopus 로고    scopus 로고
    • Use of exchangeable pairs in the analysis of simulations
    • Institute of Mathematical Statistics
    • C. Stein, P. Diaconis, S. Holmes, G. Reinert, et al Use of exchangeable pairs in the analysis of simulations. In Stein's Method, pages 1-25. Institute of Mathematical Statistics, 2004.
    • (2004) Stein's Method , pp. 1-25
    • Stein, C.1    Diaconis, P.2    Holmes, S.3    Reinert, G.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.