approximation algorithms
Approximation algorithms in AI are algorithms designed to find near-optimal solutions to complex optimization problems that are computationally expensive to solve exactly, especially in cases where resources or time are limited.
- Fair Minimum Labeling: Efficient Temporal Network Activations for Reachability and Equity
- Faster Algorithms for Structured John Ellipsoid Computation
- Improved Algorithms for Fair Matroid Submodular Maximization
- Improved Approximation Algorithms for Chromatic and Pseudometric-Weighted Correlation Clustering
- Learning-Augmented Online Bidding in Stochastic Settings
- New Parallel and Streaming Algorithms for Directed Densest Subgraph
- Unifying Proportional Fairness in Centroid and Non-Centroid Clustering