—Today’s enterprise data centers are shifting towards a utility computing model where many business critical applications share a common pool of infrastructure resources that o...
Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common...
Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen...
The recent advances in miniaturization and the creation of low-power circuits, combined with small-sized batteries have made the development of wireless sensor networks a working ...
We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). O...
In this paper, we study online algorithms when the input is not chosen adversarially, but consists of draws from some given probability distribution. While this model has been stu...