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Exploratory Landscape Validation for Bayesian Optimization Algorithms
MathematicsBayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of ...
Taleh Agasiev, Anatoly Karpenko
doaj +1 more source
MethodsX, 2019 Accurate and reliable predictions of bacterial growth and metabolism from unstructured kinetic models are critical to the proper operation and design of engineered biological treatment and remediation systems. As such, parameter estimation has progressed
Derek C. Manheim, Russell L. Detwiler
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Approximate Bayesian Inference Based on Expected Evaluations [PDF]
, 2023 Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable.
Hammer, Hugo Lewi, Riegler, Michael
core
Bayesian Optimization in AlphaGo
CoRR, 2018 During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times. This automatic tuning process resulted in substantial improvements in playing strength. For example, prior to the match with Lee Sedol, we tuned the latest AlphaGo agent and this improved its win-rate from 50% to 66.5% in self-play games ...
Yutian Chen 0001 +6 more
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Triangulation Candidates for Bayesian Optimization
Advances in Neural Information Processing Systems 35, 2022 Bayesian optimization involves "inner optimization" over a new-data acquisition criterion which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart local numerical optimizers. In such cases it is common to replace continuous search with a discrete one over random candidates.
Robert B. Gramacy +2 more
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Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context [PDF]
, 2010 A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue.
Pitiot, Paul +3 more
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Bayesian Approximations to Hidden Semi-Markov Models for Telemetric Monitoring of Physical Activity [PDF]
, 2022 We propose a Bayesian hidden Markov model for analyzing time series and sequential data where a special structure of the transition probability matrix is embedded to model explicit-duration semi-Markovian dynamics.
Fiecas, Mark +2 more
core +1 more source
Bayesian Functional Optimization
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Bayesian optimization (BayesOpt) is a derivative-free approach for sequentially optimizing stochastic black-box functions. Standard BayesOpt, which has shown many successes in machine learning applications, assumes a finite dimensional domain which often is a parametric space.
Vien, Ngo Anh +2 more
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Bayesian Optimization for Conformer Generation [PDF]
Journal of Cheminformatics, 2018 Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima.
Lucian Chan +2 more
openaire +4 more sources