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...
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...
Spectral hypergraph sparsification, which is an attempt to extend well-k...
Bayesian persuasion is a model for understanding strategic information
r...
We address Stackelberg models of combinatorial congestion games (CCGs); ...
We consider making outputs of the greedy algorithm for monotone submodul...
We consider making outputs of the greedy algorithm for monotone submodul...
Machine learning is increasingly being used in various applications that...
Non-convex sparse minimization (NSM), or ℓ_0-constrained minimization of...
Submodular maximization with a cardinality constraint can model various
...
The stochastic greedy algorithm (SG) is a randomized version of the gree...
We propose a new concept named adaptive submodularity ratio to study the...
Non-convex constrained optimization problems have many applications in
m...