Page 171 - 《软件学报》2024年第4期
P. 171

钱鸿  等:  基于动态批量评估的绿色无梯度优化方法                                                       1749


         [36]    Li L,  Jamieson K, Rostamizadeh A,  et al.  A system  for  massively parallel hyperparameter tuning. In:  Proc.  of the  Machine
              Learning and Systems 2020. 2020.
         [37]    Li S, Xing W, Kirby R, et al. Multi-fidelity Bayesian optimization via deep neural networks. In: Advances in Neural Information
              Processing Systems 33. 2020.
         [38]    Belakaria S, Deshwal A, Doppa J. Multi-fidelity multi-objective Bayesian optimization: An output space entropy search approach.
              In: Proc. of the 34th AAAI Conf. on Artificial Intelligence. 2020. 10035−10043.
         [39]    Takeno S, Fukuoka H, Tsukada Y, et al. Takeuchi I, Karasuyama M. Multi-fidelity Bayesian optimization with max-value entropy
              search and its parallelization. In: Proc. of the 37th Int’l Conf. on Machine Learning, 2020. 9334−9345.
         [40]    Roux N, Schmidt  M,  Bach F. A stochastic gradient  method  with  an  exponential  convergence rate for finite training  sets. In:
              Advances in Neural Information Processing Systems 25. 2012. 2672−2680.
         [41]    Qian C. Distributed Pareto optimization for large-scale noisy subset selection. IEEE Trans. on Evolutionary Computation, 2020,
              24(4): 694−707.
         [42]    Liu Y,  Hu YQ, Qian H,  et al. ZOOpt:  A toolbox  for derivative-free optimization.  Science  China Information Sciences,  2022,
              65(10).
         [43]    Johnson R,  Zhang T. Accelerating stochastic  gradient descent using predictive variance  reduction. In:  Advances in  Neural
              Information Processing Systems 26. 2013. 315−323.
         [44]    Defazio A, Bach F, Lacoste-Julien S. SAGA: A fast incremental gradient method with support for non-strongly convex composite
              objectives. In: Advances in Neural Information Processing Systems 27. 2014. 1646−1654.
         [45]    Hansen  N,  Müller S, Koumoutsakos P. Reducing the time  complexity of the derandomized evolution strategy with  covariance
              matrix adaptation (CMA-ES). Evolutionary Computation, 2003, 11(1): 1−18.
         [46]    Bäck T, Schwefel H. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1993, 1(1):
              1−23.
         [47]    Jin Y, Wang H, Chugh T, et al. Data-driven evolutionary optimization: An overview and case studies. IEEE Trans. on Evolutionary
              Computation, 2018, 23(2): 442−458.
         [48]    Shahriari B, Swersky K, Wang Z, et al. Taking the human out of the loop: A review of Bayesian optimization. Proc. of the IEEE,
              2016, 104(1): 148−175.
         [49]    Garnett R. Bayesian Optimization. Cambridge University Press, 2023.
         [50]    Bartlett P, Gabillon V, Valko M. A simple parameter-free and adaptive approach to optimization under a minimal local smoothness
              assumption. In: Proc. of the 2019 Algorithmic Learning Theory. 2019. 184−206.
         [51]    Munos R. From bandits to Monte-Carlo tree search: The optimistic principle applied to optimization and planning. Foundations and
              Trends in Machine Learning, 2014, 7(1): 1−129.
         [52]    Valko M, Carpentier A, Munos R. Stochastic simultaneous optimistic optimization. In: Proc. of the 30th Int’l Conf. on Machine
              Learning 2013. 2013. 19−27.
         [53]    Jones D, Perttunen C, Stuckman B. Lipschitzian optimization without the Lipschitz constant. Journal of Optimization Theory and
              Applications, 1993, 79: 157−181.
         [54]    Boer P, Kroese D, Mannor S, et al. A tutorial on the cross-entropy method. Annals of Operations Research, 2005, 134(1).
         [55]    Hansen N,  Ostermeier A. Adapting arbitrary  normal mutation  distributions in evolution  strategies: The covariance matrix
              adaptation. In: Proc. of the 1996 IEEE Int’l Conf. on Evolutionary Computation. Nayoya University, 1996. 312−317.
         [56]    Hansen N, Ostermeier A. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 2001, 9(2):
              159−195.
         [57]    Li L, Jamieson  K, DeSalvo  G,  et al.  Hyperband: A novel bandit-based approach to hyperparameter optimization. Journal of
              Machine Learning Research, 2017, 18(185): 1−52.
         [58]    Lindauer M, Eggensperger K,  Feurer M,  et al. SMAC3:  A versatile  Bayesian optimization  package for hyperparameter
              optimization. Journal of Machine Learning Research, 2022, 23(54): 1−9.
         [59]    González  J, Dai Z, Hennig  P,  et al. Batch  Bayesian  optimization via  local penalization. In:  Proc.  of the 19th  Int’l  Conf.  on
              Artificial Intelligence and Statistics. 2016. 648−657.
   166   167   168   169   170   171   172   173   174   175   176