We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as...
We propose a new method for estimating intrinsic dimension of a dataset derived by applying the principle of maximum likelihood to the distances between close neighbors. We derive...
CBR is one of the techniques that can be applied to the task of approximating a function over high-dimensional, continuous spaces. In Reinforcement Learning systems a learning agen...
We present a simulation-based semi-formal verification method for sequential circuits described at the registertransfer level. The method consists of an iterative loop where cove...
Serdar Tasiran, Farzan Fallah, David G. Chinnery, ...
The sliding window model is useful for discounting stale data in data stream applications. In this model, data elements arrive continually and only the most recent N elements are ...
Brian Babcock, Mayur Datar, Rajeev Motwani, Liadan...