2 citations · 4 across the 5 of their papers we have counts for
5 papers
Efficient parallel implementation of the multiplicative weight update method for graph-based linear programs
Caleb Ju, Serif Yesil, Mengyuan Sun +2
Positive linear programs (LPs) model many graph and operations research problems. One can solve for a -approximation for positive LPs, for any selected , in polylogarithm…
Dual dynamic programming for stochastic programs over an infinite horizon
Caleb Ju, Guanghui Lan
We consider solving stochastic programs over an infinite horizon. By leveraging the stationarity of the problem, we develop a novel continually-exploring infinite-horizon explorati…
Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data
Yan Li, Caleb Ju, Ethan X. Fang +1
Bregman proximal point algorithm (BPPA) has witnessed emerging machine learning applications, yet its theoretical understanding has been largely unexplored. We study the computatio…
Communication lower bounds for nested bilinear algorithms via rank expansion of Kronecker products
Caleb Ju, Yifan Zhang, Edgar Solomonik
We develop lower bounds on communication in the memory hierarchy or between processors for nested bilinear algorithms, such as Strassen's algorithm for matrix multiplication. We bu…
Derivation and Analysis of Fast Bilinear Algorithms for Convolution
Caleb Ju, Edgar Solomonik
The prevalence of convolution in applications within signal processing, deep neural networks, and numerical solvers has motivated the development of numerous fast convolution algor…