We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic nite automata...
We consider the problem of selecting an optimal set of sensors, as determined, for example, by the predictive accuracy of the resulting sensor network. Given an underlying metric ...
Roman Garnett, Michael A. Osborne, Stephen J. Robe...
Let f be a function on Rd that is monotonic in every variable. There are 2d possible assignments to the directions of monotonicity (two per variable). We provide sufficient condit...
Generating good, production-quality plans is an essential element in transforming planners from research tools into real-world applications, but one that has been frequently overl...
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...