Motivated by the capabilities of modern storage architectures, we consider the following generalization of the data stream model where the algorithm has sequential access to multi...
An open issue in data-driven dependency parsing is how to handle non-projective dependencies, which seem to be required by linguistically adequate representations, but which pose ...
We present a fast algorithm for computing approximate quantiles in high speed data streams with deterministic error bounds. For data streams of size N where N is unknown in advanc...
Uncertain data streams are increasingly common in real-world deployments and monitoring applications require the evaluation of complex queries on such streams. In this paper, we c...
Thanh T. L. Tran, Andrew McGregor, Yanlei Diao, Li...
Imitation Learning, while applied successfully on many large real-world problems, is typically addressed as a standard supervised learning problem, where it is assumed the trainin...