# Experimental Design for Any p-Norm

arXiv: https://arxiv.org/abs/2305.01942 · HTML: https://mlsanity.com/paper/2305.01942/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 6 of 6)

- [Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach](https://mlsanity.com/paper/1711.05174/) (2017)
- [Bayesian experimental design using regularized determinantal point processes](https://mlsanity.com/paper/1906.04133/) (2019)
- [Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design](https://mlsanity.com/paper/1802.08318/) (2018)
- [Approximation Algorithms for D-optimal Design](https://mlsanity.com/paper/1802.08372/) (2018)
- [A Spectral Approach to Network Design](https://mlsanity.com/paper/2003.07810/) (2020)
- [A Local Search Framework for Experimental Design](https://mlsanity.com/paper/2010.15805/) (2020)

## Built on by (most cited first, 1 of 1)

- [Experimental Design Using Interlacing Polynomials](https://mlsanity.com/paper/2410.11390/) (2024)

## Abstract (arXiv)

We consider a general p-norm objective for experimental design problems that captures some well-studied objectives (D/A/E-design) as special cases. We prove that a randomized local search approach provides a unified algorithm to solve this problem for all p. This provides the first approximation algorithm for the general p-norm objective, and a nice interpolation of the best known bounds of the special cases.