We describe a primal-dual framework for the design and analysis of online strongly convex optimization algorithms. Our framework yields the tightest known logarithmic regret bound...
—This paper presents a system-level Network-on-Chip modeling framework that integrates transaction-level model and analytical wire model for design space exploration. It enables ...
To develop effective learning algorithms for online cursive word recognition is still a challenge research issue. In this paper, we propose a probabilistic framework to model the ...
Maintaining statistics on multidimensional data distributions is crucial for predicting the run-time and result size of queries and data analysis tasks with acceptable accuracy. To...
Developing concurrent programs is intrinsically difficult. They are subject to programming errors that are not present in traditional sequential programs. Our current work is to ...