Parameter Estimation for Generalized Low-Rank Matrix Sensing by Learning on Riemannian Manifolds
<|reference_start|>Parameter Estimation for Generalized Low-Rank Matrix Sensing by Learning on Riemannian Manifolds: We prove convergence guarantees for generalized low-rank matrix sensing -- i.e., where matrix sensing where the observations may be passed through some nonlinear link function. We focus on local convergence of the optimal estimator, ignoring questions of optimization. In particular, assuming the minimizer of the empirical loss $\theta^0$ is in a constant size ball around the true parameters $\theta^*$, we prove that $d(\theta^0,\theta^*)=\tilde{O}(\sqrt{dk^2/n})$. Our analysis relies on tools from Riemannian geometry to handle the rotational symmetry in the parameter space.<|reference_end|>
@article{bastani2024parameter,
    title={Parameter Estimation for Generalized Low-Rank Matrix Sensing by Learning
  on Riemannian Manifolds},
    author={Osbert Bastani},
    journal={arXiv preprint arXiv:2407.10238},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10238},
    primaryClass={stat.ML cs.LG}
}