This paper studies a simple data-driven approach to high-dimensional lin...
Recent years have seen a growing interest in accelerating optimization
a...
An emerging line of work has shown that machine-learned predictions are
...
Learning sketching matrices for fast and accurate low-rank approximation...
Matrix representations are a powerful tool for designing efficient algor...
The maximum a posteriori (MAP) inference for determinantal point process...
Greedy best-first search (GBFS) and A* search (A*) are popular algorithm...
Augmenting algorithms with learned predictions is a promising approach f...
We study the generalized minimum Manhattan network (GMMN) problem: given...
The matrix-tree theorem counts the number of spanning trees of undirecte...
Skew polynomials, which have a noncommutative multiplication rule betwee...
Differential-algebraic equations (DAEs) are widely used for modeling of
...
Many signals on Cartesian product graphs appear in the real world, such ...