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Bayesian optimization (BO) is an efficient framework for solving black-box optimization problems with expensive function evaluations.
Combinatorial Black-Box Optimization with Expert Advice
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Fourth Test of General Relativity: preliminary results
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Low Autocorrelation Binary Sequences:Statistical Mechanics and Configuration Space Analysis
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Network Flows: Theory, Algorithms, and Applications
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Diffusion Kernels on Graphs and other Discrete Structures
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Scalable Combinatorial Bayesian Optimization with Tractable Statistical models
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Handbook of product graphs
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Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
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Bayesian Optimization of Combinatorial Structures
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Automatic Chemical Design using a Data-driven Continuous Representation of Molecules
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Parallelised Bayesian Optimisation via Thompson Sampling
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Survey of variation in Human Transcription Factors reveals prevalent DNA Binding changes
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Large-Scale Price Optimization via Network Flow
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Low Autocorrelation Binary Sequences
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Max-value Entropy Search for Multi-Objective Bayesian Optimization
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Walsh functions as surrogate model for pseudo-boolean optimization problems
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Combinatorial Bayesian Optimization using the Graph Cartesian Product
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Analysis and Modeling of UAV Power System Architectures
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Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
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Population-Based Black-Box Optimization for Biological Sequence Design
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