# Generalization in portfolio-based algorithm selection

@inproceedings{Balcan2020GeneralizationIP, title={Generalization in portfolio-based algorithm selection}, author={Maria-Florina Balcan and Tuomas Sandholm and Ellen Vitercik}, booktitle={AAAI}, year={2020} }

Portfolio-based algorithm selection has seen tremendous practical success over the past two decades. This algorithm configuration procedure works by first selecting a portfolio of diverse algorithm parameter settings, and then, on a given problem instance, using an algorithm selector to choose a parameter setting from the portfolio with strong predicted performance. Oftentimes, both the portfolio and the algorithm selector are chosen using a training set of typical problem instances from the…

## 2 Citations

### How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design

- Computer ScienceSTOC
- 2021

This work provides a broadly applicable theory for deriving generalization guarantees that bound the difference between the algorithm’s average performance over the training set and its expected performance on the unknown distribution and uncovers a unifying structure which is used to prove extremely general guarantees.

### Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning

- Computer Science
- 2020

PoSH Auto-sklearn is developed, which enables AutoML systems to work well on large datasets under rigid time limits using a new, simple and meta-feature-free meta-learning technique and employs a successful bandit strategy for budget allocation.

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