# Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities

arXiv: https://arxiv.org/abs/2004.01022 · HTML: https://mlsanity.com/paper/2004.01022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

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

- [Learning to Infer Structures of Network Games](https://mlsanity.com/paper/2206.08119/) (2022)

## Abstract (arXiv)

In this paper, we study the problem of learning the exact structure of continuous-action games with non-parametric utility functions. We propose an ℓ1 regularized method which encourages sparsity of the coefficients of the Fourier transform of the recovered utilities. Our method works by accessing very few Nash equilibria and their noisy utilities. Under certain technical conditions, our method also recovers the exact structure of these utility functions, and thus, the exact structure of the game. Furthermore, our method only needs a logarithmic number of samples in terms of the number of players and runs in polynomial time. We follow the primal-dual witness framework to provide provable theoretical guarantees.