Bayesian optimization
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
... state-of-the-art Bayesian optimization methods to solve nonlin- ear optimization, stochastic bandits or sequential experimental design ...problems. Bayesian optimization characterized ...
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Speed Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
... of Bayesian Optimization to efficiently tune the speed-related decoding parameters by eas- ily incorporating speed as a noisy constraint ...overall optimization time re- duction compared to grid and ...
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No-Regret Bayesian Optimization with Unknown Hyperparameters
... Bayesian optimization (BO) based on Gaussian process models is a powerful paradigm to optimize black-box functions that are expensive to ...During optimization we slowly adapt the hyperparameters of ...
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Towards an interactive drone : a Bayesian optimization approach
... of Bayesian optimization with the different acquisi- tion functions presents potential problems when used for learning the impedance parameters values of a ...
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MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models
... sets. Bayesian optimization techniques have been used for fast inference in state-space models with intractable ...multi-fidelity Bayesian op- timization algorithm for the inference of general ...
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Learning to select data for transfer learning with Bayesian Optimization
... If we learn a data selection measure using Bayesian Optimization, we are able to outper- form the baselines with almost all feature sets. Performance gains are considerable for all do- mains with individual ...
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Learning the Curriculum with Bayesian Optimization for Task Specific Word Representation Learning
... Popular choices for the surrogate model are Gaussian Processes (Rasmussen, 2006; Snoek et al., 2012, GP), providing convenient and powerful prior distribution on functions, and tree-structured Parzen estimators (Bergstra ...
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Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots
... Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically re- quire tuning of parameters for each task ...
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Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning
... For model-based RL we presented MBOA for model-based RL. MBOA improves on the standard BOA algorithm by using collections of trajectories to learn an approximate simulator of the decision process. The simulator is used ...
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Bayesian Optimization for Policy Search via Online-Offline Experimentation
... multi-task Bayesian optimization to tune live machine learning ...of Bayesian opti- mization efficiently tuning a live machine learning system by combining offline and online ...
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ADMMBO: Bayesian Optimization with Unknown Constraints using ADMM
... using Bayesian optimization with unconstrained acquisition ...specific Bayesian model for each subproblem objective that takes advantage of this knowledge to better guide the ...
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Bayesian optimization for fitting 3D morphable models of brain structures
... Our approach is based on the Bayesian optimization process for estimating the shape parameters that fit accurately a given brain structure in a probabilistic way. In this work, we used the 3D models of the ...
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Deep Bayesian Optimization on Attributed Graphs
... structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool in complex network ...vectorial Bayesian optimization methods ...
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Bayesian Optimization of Text Representations
... convex optimization in N |O| dimensions—a solvable problem that is costly for large datasets and/or large output ...to Bayesian optimization, a family of techniques that can be used to select ...
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Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
... using Bayesian optimization where the discrepancy is modeled with a Gaussian ...and optimization reduced the number of simulated data sets by several orders of magnitude in our ...
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Investigating Bayesian optimization for rail network optimization
... the optimization methods require many (typically >10 4 ) repeat simulations, the computational cost of optimization is dominated by ...examines Bayesian Optimization and benchmarks it ...
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Bayesian optimization for seed germination
... We applied Bayesian optimization framework to the seed germination process in a controlled environment. Our experiments demonstrated that the proposed method- ology allowed to identify the values of the ...
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A 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. Of particular interest to us is to efficiently solve problems with decoupled ...
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Optimization of the SHiP Spectrometer Tracker geometry using the Bayesian Optimization with Gaussian Processes
... eter Tracker geometry optimization using Bayesian optimization with Gaussian 13.. processes in considered.[r] ...
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Bayesian Optimization Algorithm for Non-unique Oligonucleotide Probe Selection
... The probes for hybridization experiments were selected mostly randomly or based on the frequency of occurance of probes sequences in the genes before the work of Herweig et al. [18]. Other criteria such as G+C content ...
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