We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
In this paper, we propose a novel adaptive step-size approach for policy gradient reinforcement learning. A new metric is defined for policy gradients that measures the effect of ...
Takamitsu Matsubara, Tetsuro Morimura, Jun Morimot...
In this paper, we study a sequential decision making problem. The objective is to maximize the total reward while satisfying constraints, which are defined at every time step. The...
This paper presents a new technique which incrementally builds a hierarchical discriminant regression (IHDR) tree for generation of motion based robot reactions. The robot learned...
When using machine learning for in silico modeling, the goal is normally to obtain highly accurate predictive models. Often, however, models should also bring insights into intere...