• Spearmint

  • Referenced in 98 articles [sw17859]
  • software package to perform Bayesian optimization. The Software is designed to automatically run experiments (thus...
  • Stan

  • Referenced in 286 articles [sw10200]
  • Bayesian statistical inference with MCMC sampling (NUTS, HMC) and penalized maximum likelihood estimation with Optimization...
  • Auto-WEKA

  • Referenced in 35 articles [sw21536]
  • WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. WEKA is a widely used ... using a state-of-the-art Bayesian optimization method. Our new package is tightly integrated...
  • Hyperband

  • Referenced in 17 articles [sw41120]
  • novel bandit-based approach to hyperparameter optimization. Performance of machine learning algorithms depends critically ... hyperparameters. While recent approaches use Bayesian optimization to adaptively select configurations, we focus on speeding ... allocation and early-stopping. We formulate hyperparameter optimization as a pure-exploration nonstochastic infinite-armed ... Furthermore, we compare Hyperband with popular Bayesian optimization methods on a suite of hyperparameter optimization...
  • acebayes

  • Referenced in 12 articles [sw20243]
  • acebayes: An R Package for Bayesian Optimal Design of Experiments via Approximate Coordinate Exchange ... package acebayes to find Bayesian optimal experimental designs. A decision-theoretic approach is adopted, with ... design maximising an expected utility. Finding Bayesian optimal designs for realistic problems is challenging...
  • ToulBar2

  • Referenced in 22 articles [sw07289]
  • Weighted Max-SAT, Quadratic Pseudo-Boolean Optimization, and Bayesian Networks...
  • BayesOpt

  • Referenced in 7 articles [sw12003]
  • BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits. BayesOpt ... library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits ... sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds...
  • PESC

  • Referenced in 8 articles [sw17860]
  • general framework for constrained Bayesian optimization using information-based search. We present an information-theoretic ... framework for solving global black-box optimization problems that also have black-box constraints ... towards a unified solution for constrained Bayesian optimization...
  • RStan

  • Referenced in 81 articles [sw13990]
  • rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization...
  • HumanEva

  • Referenced in 24 articles [sw15489]
  • that uses a relatively standard Bayesian framework with optimization in the form of Sequential Importance ... view laboratory environment, where initialization is available, Bayesian filtering tends to perform well. The datasets...
  • BoTorch

  • Referenced in 6 articles [sw40383]
  • Framework for Efficient Monte-Carlo Bayesian Optimization. Bayesian optimization provides sample-efficient global optimization ... BoTorch, a modern programming framework for Bayesian optimization that combines Monte-Carlo (MC) acquisition functions...
  • mlrMBO

  • Referenced in 8 articles [sw19214]
  • based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black...
  • BOCK

  • Referenced in 5 articles [sw32130]
  • BOCK : Bayesian Optimization with Cylindrical Kernels. A major challenge in Bayesian Optimization is the boundary ... this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea...
  • BOHB

  • Referenced in 5 articles [sw35481]
  • BOHB: Robust and Efficient Hyperparameter Optimization at Scale. Modern deep learning methods are very sensitive ... state-of-the-art models, vanilla Bayesian hyperparameter optimization is typically computationally infeasible ... combine the benefits of both Bayesian optimization and bandit-based methods, in order to achieve ... optimization method, which consistently outperforms both Bayesian optimization and Hyperband on a wide range...
  • GPflowOpt

  • Referenced in 4 articles [sw33396]
  • GPflowOpt: A Bayesian Optimization Library using TensorFlow. A novel Python framework for Bayesian optimization known ... differentiation, parallelization and GPU computations for Bayesian optimization. Design goals focus on a framework that ... value entropy search, as well as a Bayesian multi-objective approach. Finally, it permits easy...
  • COMBO

  • Referenced in 4 articles [sw40602]
  • COMmon Bayesian Optimization Library ( COMBO ). Bayesian optimization has been proven as an effective tool...
  • AutoKeras

  • Referenced in 6 articles [sw33648]
  • propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural...
  • rstan

  • Referenced in 41 articles [sw16103]
  • rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization...
  • GOBNILP

  • Referenced in 5 articles [sw29883]
  • GOBNILP (Globally Optimal Bayesian Network learning using Integer Linear Programming) is a C program which...
  • designv2

  • Referenced in 3 articles [sw27807]
  • involving log(potency) in comparative binary bioassays. Optimal designs are investigated for bioassays involving ... main interest. Local and Bayesian D-optimal designs are considered, as well ... prior distributions used for the Bayesian optimal designs includes uniform, trivariate normal and a bivariate ... lack of closed form solutions for Bayesian optimal designs, much of the investigation is numerical...