We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...
Many recent techniques for timing analysis under variability, in which delay is an explicit function of underlying parameters, may be described as parameterized timing analysis. T...
For more than thirty years, the parallel programming community has used the dependence graph as the main abstraction for reasoning about and exploiting parallelism in “regular...
Keshav Pingali, Donald Nguyen, Milind Kulkarni, Ma...
Let {Sm} be an infinite sequence whose limit or antilimit S can be approximated very efficiently by applying a suitable extrapolation method E0 to {Sm}. Assume that the Sm and henc...
We develop a new tool for data-dependent analysis of the exploration-exploitation trade-off in learning under limited feedback. Our tool is based on two main ingredients. The fi...