An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
— Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far d...
Model selection by the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estima...
Abstract--We present a methodology for implementing digital signal processing (DSP) operations such as filtering with biomolecular reactions. From a DSP specification, we demonstra...
Hua Jiang, Aleksandra P. Kharam, Marc D. Riedel, K...
This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain ...
Shrihari Vasudevan, Fabio T. Ramos, Eric Nettleton...