• HumanEva

  • Referenced in 23 articles [sw15489]
  • algorithm for 3D articulated tracking that uses a relatively standard Bayesian framework with optimization ... Filtering. In the context of this baseline algorithm we explore a variety of likelihood functions ... human motion and the effects of algorithm parameters. Our experiments suggest that image observation models ... view laboratory environment, where initialization is available, Bayesian filtering tends to perform well. The datasets...
  • Auto-WEKA

  • Referenced in 23 articles [sw21536]
  • WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. WEKA is a widely used ... joint space of WEKA’s learning algorithms and their respective hyperparameter settings to maximize performance ... using a state-of-the-art Bayesian optimization method. Our new package is tightly integrated ... users as any other learning algorithm...
  • SafeOpt

  • Referenced in 2 articles [sw35418]
  • adapted version of the safe, Bayesian optimization algorithm, SafeOpt [1], [2]. It also provides...
  • ADVI

  • Referenced in 23 articles [sw34040]
  • inference (ADVI). The user only provides a Bayesian model and a dataset; nothing else ... models. The algorithm automatically determines an appropriate variational family and optimizes the variational objective...
  • MOBOpt

  • Referenced in 1 article [sw33394]
  • class, that implements a multi-objective Bayesian optimization algorithm. The proposed method is able...
  • BOCK

  • Referenced in 3 articles [sw32130]
  • challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends ... this paper, we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea...
  • BUQO

  • Referenced in 4 articles [sw34653]
  • underlying convex geometry to formulate the Bayesian hypothesis test as a convex problem, which ... then efficiently solve by using scalable optimization algorithms. This allows scaling to high-resolution ... Bayesian computation approaches. We illustrate our methodology, dubbed BUQO (Bayesian Uncertainty Quantification by Optimization...
  • MODL

  • Referenced in 10 articles [sw18797]
  • MODL: A Bayes optimal discretization method for continuous attributes, While real data often comes ... format, discrete and continuous, many supervised induction algorithms require discrete data. Efficient discretization of continuous ... discretization method MODL, founded on a Bayesian approach. We introduce a space of discretization models ... optimal evaluation criterion of discretizations. We then propose a new super-linear optimization algorithm that...
  • PESC

  • Referenced in 6 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 ... algorithm that provides a promising direction towards a unified solution for constrained Bayesian optimization...
  • Hybrid Stable Spline Toolbox

  • Referenced in 7 articles [sw16024]
  • stability. The algorithm consists of a two-step procedure. First, exploiting the Bayesian interpretation ... cast as marginal likelihood optimization. We show how an approximated optimization can be efficiently performed ... Monte Carlo scheme. Then, the stable spline algorithm is used to reconstruct each subsystem. Numerical...
  • acebayes

  • Referenced in 11 articles [sw20243]
  • design maximising an expected utility. Finding Bayesian optimal designs for realistic problems is challenging ... package implements the approximate coordinate exchange (ACE) algorithm to optimise (an approximation to) the expected...
  • SUN

  • Referenced in 13 articles [sw28156]
  • trying to optimize when directing attention. The resulting model is a Bayesian framework from which ... well as or better than existing algorithms in predicting people’s fixations in free viewing...
  • LS-SVMlab

  • Referenced in 26 articles [sw07367]
  • minimization. In the methods one solves convex optimization problems, typically quadratic programs. Least Squares Support ... between kernel versions of classical pattern recognition algorithms such as kernel Fisher discriminant analysis ... into LS-SVMs where needed and a Bayesian framework with three levels of inference...
  • MADLens

  • Referenced in 1 article [sw36359]
  • forward model in Bayesian inference algorithms that require optimization or derivative-aided sampling. Another...
  • RAIcode

  • Referenced in 8 articles [sw34283]
  • constraint-based (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential ... algorithms d-separate structures and then direct the resulted undirected graph, the RAI algorithm combines ... algorithm and increases the accuracy by diminishing the curse-of-dimensionality. When the RAI algorithm ... over the PC, three phase dependency analysis, optimal reinsertion, greedy search, greedy equivalence search, sparse...
  • torcpy

  • Referenced in 1 article [sw33488]
  • data, parametric searches and algorithms used in numerical optimization and Bayesian uncertainty quantification. In this...
  • MATEDA

  • Referenced in 5 articles [sw07769]
  • optimization of single and multi-objective problems with estimation of distribution algorithms (EDAs) based ... undirected graphical models and Bayesian networks. The implementation is conceived for allowing the incorporation...
  • BioOptimizer

  • Referenced in 6 articles [sw17421]
  • comprehensive scoring function based on a full Bayesian model that can handle unknown site abundance ... variable-length gaps. An algorithm called BioOptimizer is proposed to optimize this scoring function...
  • Vprop

  • Referenced in 1 article [sw22203]
  • efficient methods for Bayesian deep learning rely on continuous optimization algorithms, but the implementation ... changes to the off-the-shelf RMSprop optimizer. Vprop also reduces the memory requirements ... efficient, and easy-to-implement method for Bayesian deep learning...
  • Pi4U

  • Referenced in 7 articles [sw18320]
  • Pi4U, an extensible framework, for non-intrusive Bayesian Uncertainty Quantification and Propagation ... Laplace asymptotic approximations as well as stochastic algorithms, along with distributed numerical differentiation and task ... Chain Monte Carlo (TMCMC) algorithm and its variants. The optimization tasks associated with the asymptotic ... applicability and efficiency of Bayesian tools when applied to computationally demanding physical models. Theoretical...