mlsanityData to 7 Oct 2026

Experimental Design for Any p-Norm

arXiv 2305.01942 ↗First posted 3 May 2023cs.DS cs.LG stat.COcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 20 references that are in the map. First posted on arXiv 3 May 2023.

Builds on

6 of 20 references.

References · 6

  1. 2017Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approachcited by 19
  2. 2019Bayesian experimental design using regularized determinantal point processescited by 17
  3. 2018Proportional Volume Sampling and Approximation Algorithms for A-Optimal Designcited by 7
  4. 2018Approximation Algorithms for D-optimal Designcited by 7
  5. 2020A Spectral Approach to Network Designcited by 2
  6. 2020A Local Search Framework for Experimental Designcited by 2

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Experimental Design Using Interlacing Polynomialscited by 0