We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a wide variety of r...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin...
This paper presents a new statistical image segmentation algorithm, in which the texture features are modeled by Symmetric Alpha-Stable (SαS) distributions. These features are ef...
Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
Shell becomes popular in a variety of modeling techniques for representing small-scale features and increasing visual complexity. Current shell generation algorithms do not measur...
Jin Huang, Xinguo Liu, Haiyang Jiang, Qing Wang, H...
Parametric yield loss due to variability can be effectively reduced by both design-time optimization strategies and by adjusting circuit parameters to the realizations of variable...
Murari Mani, Ashish Kumar Singh, Michael Orshansky