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Bayesian optimization
Bayesian optimization is a sequential design strategyfor global optimization of black-box functions that doesn't require derivatives.
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Related topics
Related topics
12 relations
Bayesian experimental design
Deep learning
Derivative-free optimization
Global optimization
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Broader (2)
Machine learning
Stochastic optimization
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Estimating Local Function Complexity via Mixture of Gaussian Processes
Danny Panknin
,
Shinichi Nakajima
,
T. Bui
,
Klaus-Robert Müller
2019
Corpus ID: 199577711
Real world data often exhibit inhomogeneity, e.g., the noise level, the sampling distribution or the complexity of the target…
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2018
2018
High-variance multivariate time series forecasting using machine learning
Nikola Katardjiev
2018
Corpus ID: 49350126
There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is…
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2018
2018
Knowledge gradient for expensive locally quadratic functions and stochastic optimization of aid allocation
N. Aboagye
2018
Corpus ID: 86771739
In this dissertation, we study the behavior of value of information policies in the presence of a locally quadratic belief model…
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2017
2017
Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization
Adam McCarthy
,
B. Rodríguez
,
A. Mincholé
2017
Corpus ID: 88523397
Performing inference over simulators is generally intractable as their runtime means we cannot compute a marginal likelihood. We…
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2014
2014
Optimizing Without Derivatives: What Does the No Free Lunch Theorem Actually Say?
L. Serafino
2014
Corpus ID: 17688853
One of the most important stages in many areas of engineering and applied sciences is modeling and the use of optimization…
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Review
2013
Review
2013
Robot Learning : Some Recent Examples
G. Konidaris
,
S. Kuindersma
,
S. Niekum
,
R. Grupen
,
A. Barto
2013
Corpus ID: 1306695
This paper provides a brief overview of three recent contributions to robot learning developed by researchers at the University…
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2013
2013
Active Tactile Exploration for Grasping
Filipe Veiga
,
Alexandre Bernardino
2013
Corpus ID: 17825041
This paper addresses the problem of robotic grasp optimization. Due to uncertainty both on the robot kinematics, motor control…
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2011
2011
Opportunity Cost in Bayesian Optimization
Jasper Snoek
,
H. Larochelle
,
Ryan P. Adams
2011
Corpus ID: 18883422
A major advantage of Bayesian optimization is that it generally requires fewer function evaluations than optimization methods…
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2007
2007
FIR Filter Design via Linear Complementarity Problem, Messy Genetic Algorithm, and Ising Messy Genetic Algorithm
A. M. Al-Fahed Nuseirat
,
R. Abu-Zitar
2007
Corpus ID: 2892348
— In this paper the design of maximally flat linear phase finite impulse response (FIR) filters is considered. The problem is…
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2000
2000
A C++ Implementation of the Bayesian Optimization Algorithm (BOA) with Decision Graphs
M. Pelikan
2000
Corpus ID: 59802165
The paper explains how to download compile and use the C implementation of the Bayesian optimization algorithm BOA with decision…
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