Abstract— We consider the problem of optimizing the trajectory of a mobile sensor with perfect localization whose task is to estimate a stochastic, perhaps multidimensional fiel...
Abstract. In contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at ...
The task of finding the optimum of some function f(x) is commonly accomplished by generating and testing sample solutions iteratively, choosing each new sample x heuristically on t...
Neural spike trains present challenges to analytical efforts due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smooth...
John P. Cunningham, Byron M. Yu, Krishna V. Shenoy...
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...