algorithmic framework
An algorithmic framework outlines the structure and design principles guiding the development and application of algorithms. It provides a foundation for understanding how various algorithms relate and can be combined for problem-solving.
- A Learning-Augmented Approach to Online Allocation Problems
- A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input
- Contextual Thompson Sampling via Generation of Missing Data
- Learning-Augmented Algorithms for $k$-median via Online Learning
- Machine Unlearning under Overparameterization
- Sample-Adaptivity Tradeoff in On-Demand Sampling
- Tight Bounds for Answering Adaptively Chosen Concentrated Queries