
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...

AutoWEKA
 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 stateoftheart 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 multiobjective 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 highresolution ... 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 superlinear optimization algorithm that...

PESC
 Referenced in 6 articles
[sw17860]
 general framework for constrained Bayesian optimization using informationbased search. We present an informationtheoretic ... framework for solving global blackbox optimization problems that also have blackbox 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 twostep 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...

LSSVMlab
 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 LSSVMs 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 derivativeaided sampling. Another...

RAIcode
 Referenced in 8 articles
[sw34283]
 constraintbased (CB) Bayesian network structure learning. The RAI algorithm learns the structure by sequential ... algorithms dseparate structures and then direct the resulted undirected graph, the RAI algorithm combines ... algorithm and increases the accuracy by diminishing the curseofdimensionality. 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 multiobjective 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 ... variablelength 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 offtheshelf RMSprop optimizer. Vprop also reduces the memory requirements ... efficient, and easytoimplement method for Bayesian deep learning...

Pi4U
 Referenced in 7 articles
[sw18320]
 Pi4U, an extensible framework, for nonintrusive 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...