Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
We consider the estimation problem in Gaussian graphical models with arbitrary structure. We analyze the Embedded Trees algorithm, which solves a sequence of problems on tractable...
Venkat Chandrasekaran, Jason K. Johnson, Alan S. W...
We introduce a new set of probabilistic analysis tools based on the analysis of And-Or trees with random inputs. These tools provide a unifying, intuitive, and powerful framework ...
Michael Luby, Michael Mitzenmacher, Mohammad Amin ...
In this paper, we introduce new algorithms for selecting taxon samples from large evolutionary trees, maintaining uniformity and randomness, under certain new constraints on the t...
Anupam Bhattacharjee, Zalia Shams, Kazi Zakia Sult...
We derive a sufficient condition for a sparse graph G on n vertices to contain a copy of a tree T of maximum degree at most d on (1 − )n vertices, in terms of the expansion prop...