메뉴 건너뛰기




Volumn 13-17-August-2016, Issue , 2016, Pages 2065-2074

FLASH: Fast Bayesian optimization for data analytic pipelines

Author keywords

Automated hyperparameter tuning; Bayesian optimization; Data analytic pipeline; Health analytics

Indexed keywords

ALGORITHMS; BUDGET CONTROL; DATA MINING; PIPELINES;

EID: 84985000572     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2939672.2939829     Document Type: Conference Paper
Times cited : (31)

References (48)
  • 3
    • 84857855190 scopus 로고    scopus 로고
    • Random search for hyperparameter optimization
    • J. Bergstra and Y. Bengio. Random search for hyperparameter optimization. JMLR, 13 (1), 2012.
    • (2012) JMLR , vol.13 , Issue.1
    • Bergstra, J.1    Bengio, Y.2
  • 4
    • 84897558007 scopus 로고    scopus 로고
    • Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
    • J. Bergstra, D. Yamins, and D. Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In ICML, 2013.
    • (2013) ICML
    • Bergstra, J.1    Yamins, D.2    Cox, D.3
  • 8
    • 84855599508 scopus 로고    scopus 로고
    • Maximizing a monotone submodular function subject to a matroid constraint
    • G. Calinescu, C. Chekuri, M. Pal, and J. Vondrak. Maximizing a monotone submodular function subject to a matroid constraint. SIAM Journal on Computing, 40 (6), 2011.
    • (2011) SIAM Journal on Computing , vol.40 , Issue.6
    • Calinescu, G.1    Chekuri, C.2    Pal, M.3    Vondrak, J.4
  • 11
    • 84982178398 scopus 로고    scopus 로고
    • Technical report, University of California Berkeley
    • D. Donoho. 50 years of Data Science. Technical report, University of California Berkeley, 2015.
    • (2015) 50 Years of Data Science
    • Donoho, D.1
  • 14
    • 85007221118 scopus 로고    scopus 로고
    • Initializing Bayesian hyperparameter optimization via meta-learning
    • M. Feurer, T. Springenberg, and F. Hutter. Initializing Bayesian hyperparameter optimization via meta-learning. In AAAI, 2015.
    • (2015) AAAI
    • Feurer, M.1    Springenberg, T.2    Hutter, F.3
  • 15
    • 84864066431 scopus 로고    scopus 로고
    • Robust design of biological experiments
    • P. Flaherty, A. Arkin, and M. I. Jordan. Robust design of biological experiments. In NIPS, 2005.
    • (2005) NIPS
    • Flaherty, P.1    Arkin, A.2    Jordan, M.I.3
  • 16
    • 84890011285 scopus 로고    scopus 로고
    • Efficiency for regularization parameter selection in penalized likelihood estimation of misspecified models
    • C. J. Flynn, C. M. Hurvich, and J. S. Simonoff. Efficiency for regularization parameter selection in penalized likelihood estimation of misspecified models. JASA, 108 (503), 2013.
    • (2013) JASA , vol.108 , Issue.503
    • Flynn, C.J.1    Hurvich, C.M.2    Simonoff, J.S.3
  • 18
    • 72949097899 scopus 로고    scopus 로고
    • Laplacian regularized d-optimal design for active learning and its application to image retrieval
    • X. He. Laplacian regularized d-optimal design for active learning and its application to image retrieval. Image Processing, IEEE Transactions on, 19 (1), 2010.
    • (2010) Image Processing, IEEE Transactions on , vol.19 , Issue.1
    • He, X.1
  • 19
    • 84907024756 scopus 로고    scopus 로고
    • Marble: High-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization
    • J. C. Ho, J. Ghosh, and J. Sun. Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization. In KDD, 2014.
    • (2014) KDD
    • Ho, J.C.1    Ghosh, J.2    Sun, J.3
  • 20
    • 80053160717 scopus 로고    scopus 로고
    • Portfolio allocation for Bayesian optimization
    • Citeseer
    • M. D. Hoffman, E. Brochu, and N. de Freitas. Portfolio allocation for Bayesian optimization. In UAI. Citeseer, 2011.
    • (2011) UAI
    • Hoffman, M.D.1    Brochu, E.2    De Freitas, N.3
  • 21
    • 84955451375 scopus 로고    scopus 로고
    • On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
    • M. D. Hoffman, B. Shahriari, and N. de Freitas. On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. In AISTATS, 2014.
    • (2014) AISTATS
    • Hoffman, M.D.1    Shahriari, B.2    De Freitas, N.3
  • 23
    • 84897524603 scopus 로고    scopus 로고
    • Revisiting frank-wolfe: Projection-free sparse convex optimization
    • M. Jaggi. Revisiting frank-wolfe: Projection-free sparse convex optimization. In ICML, 2013.
    • (2013) ICML
    • Jaggi, M.1
  • 24
    • 84908279482 scopus 로고    scopus 로고
    • Hyperoptsklearn: Automatic hyperparameter configuration for scikitlearn
    • B. Komer, J. Bergstra, and C. Eliasmith. Hyperoptsklearn: Automatic hyperparameter configuration for scikitlearn. In ICML workshop on AutoML, 2014.
    • (2014) ICML Workshop on AutoML
    • Komer, B.1    Bergstra, J.2    Eliasmith, C.3
  • 25
    • 79960793022 scopus 로고    scopus 로고
    • Submodularity and its applications in optimized information gathering
    • A. Krause and C. Guestrin. Submodularity and its applications in optimized information gathering. TIST, 2 (4), 2011.
    • (2011) TIST , vol.2 , Issue.4
    • Krause, A.1    Guestrin, C.2
  • 26
    • 85015199015 scopus 로고    scopus 로고
    • Model selection management systems: The next frontier of advanced analytics
    • A. Kumar, R. McCann, J. Naughton, and J. M. Patel. Model selection management systems: The next frontier of advanced analytics. ACM SIGMOD Record, 2015.
    • (2015) ACM SIGMOD Record
    • Kumar, A.1    McCann, R.2    Naughton, J.3    Patel, J.M.4
  • 28
    • 84891836073 scopus 로고    scopus 로고
    • Model selection principles in misspecified models
    • J. Lv and J. S. Liu. Model selection principles in misspecified models. JRSS-B, 76 (1), 2014.
    • (2014) JRSS-B , vol.76 , Issue.1
    • Lv, J.1    Liu, J.S.2
  • 29
    • 84963745941 scopus 로고    scopus 로고
    • Sequential pattern mining of electronic healthcare reimbursement claims: Experiences and challenges in uncovering how patients are treated by physicians
    • Oct
    • K. Malhotra, T. Hobson, S. Valkova, L. Pullum, and A. Ramanathan. Sequential pattern mining of electronic healthcare reimbursement claims: Experiences and challenges in uncovering how patients are treated by physicians. In Big Data, Oct 2015.
    • (2015) Big Data
    • Malhotra, K.1    Hobson, T.2    Valkova, S.3    Pullum, L.4    Ramanathan, A.5
  • 32
    • 0342813049 scopus 로고
    • The application of Bayesian methods for seeking the extremum
    • J. Mockus, V. Tiesis, and A. Zilinskas. The application of Bayesian methods for seeking the extremum. Towards Global Optimization, 2 (117-129), 1978.
    • (1978) Towards Global Optimization , vol.2 , pp. 117-129
    • Mockus, J.1    Tiesis, V.2    Zilinskas, A.3
  • 33
    • 85162504694 scopus 로고    scopus 로고
    • Optimistic optimization of deterministic functions without the knowledge of its smoothness
    • R. Munos. Optimistic optimization of deterministic functions without the knowledge of its smoothness. In Advances in neural information processing systems, 2011.
    • (2011) Advances in Neural Information Processing Systems
    • Munos, R.1
  • 35
    • 84899483965 scopus 로고    scopus 로고
    • Paramo: A parallel predictive modeling platform for healthcare analytic research using electronic health records
    • K. Ng, A. Ghoting, S. R. Steinhubl, W. F. Stewart, B. Malin, and J. Sun. Paramo: A parallel predictive modeling platform for healthcare analytic research using electronic health records. Journal of biomedical informatics, 48, 2014.
    • (2014) Journal of Biomedical Informatics , vol.48
    • Ng, K.1    Ghoting, A.2    Steinhubl, S.R.3    Stewart, W.F.4    Malin, B.5    Sun, J.6
  • 40
    • 79953144587 scopus 로고    scopus 로고
    • Greedy sensor selection: Leveraging submodularity
    • M. Shamaiah, S. Banerjee, and H. Vikalo. Greedy sensor selection: Leveraging submodularity. In CDC, 2010.
    • (2010) CDC
    • Shamaiah, M.1    Banerjee, S.2    Vikalo, H.3
  • 41
    • 84869201485 scopus 로고    scopus 로고
    • Practical Bayesian optimization of machine learning algorithms
    • J. Snoek, H. Larochelle, and R. P. Adams. Practical Bayesian optimization of machine learning algorithms. In NIPS, 2012.
    • (2012) NIPS
    • Snoek, J.1    Larochelle, H.2    Adams, R.P.3
  • 44
    • 85018371540 scopus 로고    scopus 로고
    • Auto-weka: Combined selection and hyperparameter optimization of classification algorithms
    • C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown. Auto-weka: Combined selection and hyperparameter optimization of classification algorithms. In KDD, 2013.
    • (2013) KDD
    • Thornton, C.1    Hutter, F.2    Hoos, H.H.3    Leyton-Brown, K.4
  • 45
    • 67650938640 scopus 로고    scopus 로고
    • An informational approach to the global optimization of expensive-toevaluate functions
    • J. Villemonteix, E. Vazquez, and E. Walter. An informational approach to the global optimization of expensive-toevaluate functions. Journal of Global Optimization, 44 (4), 2009.
    • (2009) Journal of Global Optimization , vol.44 , Issue.4
    • Villemonteix, J.1    Vazquez, E.2    Walter, E.3
  • 46
    • 84896058897 scopus 로고    scopus 로고
    • Bayesian optimization in high dimensions via random embeddings
    • Citeseer
    • Z. Wang, M. Zoghi, F. Hutter, D. Matheson, and N. De Freitas. Bayesian optimization in high dimensions via random embeddings. In IJCAI. Citeseer, 2013.
    • (2013) IJCAI
    • Wang, Z.1    Zoghi, M.2    Hutter, F.3    Matheson, D.4    De Freitas, N.5
  • 48
    • 0002644952 scopus 로고
    • Maximum likelihood estimation of misspecified models
    • H. White. Maximum likelihood estimation of misspecified models. Econometrica, 1982.
    • (1982) Econometrica
    • White, H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.