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Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design
Crucially, this allows us to efficiently break down the complexity of the combinatorial domain into individual decision sets, making $\textbf{GameOpt}$ scalable to large combinatorial spaces.
Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory
The principal task to control dynamical systems is to ensure their stability.
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features
We develop an efficient and provably no-regret Bayesian optimization (BO) algorithm for optimization of black-box functions in high dimensions.
Learning the Correction for Multi-Path Deviations in Time-of-Flight Cameras
Secondly, we used this dataset to construct a learning model to predict real valued corrections to the ToF data using Random Forests.
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