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Found 5 papers, 1 papers with code
Practical First-Order Bayesian Optimization Algorithms
Such methods assume Gaussian process (GP) models for both, the function and its gradient, and use them to construct an acquisition function that identifies the next query point.
Bayesian Optimization for Function Compositions with Applications to Dynamic Pricing
Bayesian Optimization (BO) is used to find the global optima of black box functions.
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm
Learning optimal behavior from existing data is one of the most important problems in Reinforcement Learning (RL).
An Online Sample Based Method for Mode Estimation using ODE Analysis of Stochastic Approximation Algorithms
In many of the practical applications, the analytical form of the density is not known and only the samples from the distribution are available.
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks
We consider the problem of tracking an intruder using a network of wireless sensors.
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