30 citations · 148 across the 22 of their papers we have counts for
5 papers · 1 filter
Tight Lower Bounds for Directed Cut Sparsification and Distributed Min-Cut
Yu Cheng, Max Li, Honghao Lin +3
In this paper, we consider two fundamental cut approximation problems on large graphs. We prove new lower bounds for both problems that are optimal up to logarithmic factors. The f…
Sparsification of Directed Graphs via Cut Balance
Ruoxu Cen, Yu Cheng, Debmalya Panigrahi +1
In this paper, we consider the problem of designing cut sparsifiers and sketches for directed graphs. To bypass known lower bounds, we allow the sparsifier/sketch to depend on the…
Robust Learning of Fixed-Structure Bayesian Networks
Yu Cheng, Ilias Diakonikolas, Daniel Kane +1
We investigate the problem of learning Bayesian networks in a robust model where an -fraction of the samples are adversarially corrupted. In this work, we study the fully observ…
Spectral Sparsification of Random-Walk Matrix Polynomials
Dehua Cheng, Yu Cheng, Yan Liu +2
We consider a fundamental algorithmic question in spectral graph theory: Compute a spectral sparsifier of random-walk matrix-polynomial whe…
Scalable Parallel Factorizations of SDD Matrices and Efficient Sampling for Gaussian Graphical Models
Dehua Cheng, Yu Cheng, Yan Liu +2
Motivated by a sampling problem basic to computational statistical inference, we develop a nearly optimal algorithm for a fundamental problem in spectral graph theory and numerical…