First poly-time algorithm for dihedral and projected MRA via recursive method of moments on the third moment tensor, conditional on a verifiable rank conjecture for power-of-two lengths.
Diagonally-weighted generalized method of moments estimation for Gaussian mixture modeling.arXiv preprint arXiv:2507.20459, 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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SGR-GMM introduces spectral gradient reweighting via an entropy-regularized spectral game to create a robust GMM estimator, with proven convergence and finite-sample error bounds under contamination.
In low-SNR Gaussian latent-variable models, optimally weighted GMoM using minimal-order moments achieves the same leading asymptotic covariance as MLE via matching layerwise expansions of the information operators.
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Beyond Frequency Marching: Orbit Recovery in Dihedral and Projected Multireference Alignment
First poly-time algorithm for dihedral and projected MRA via recursive method of moments on the third moment tensor, conditional on a verifiable rank conjecture for power-of-two lengths.
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Robust Moment-Based Estimation via Spectral Gradient Reweighting
SGR-GMM introduces spectral gradient reweighting via an entropy-regularized spectral game to create a robust GMM estimator, with proven convergence and finite-sample error bounds under contamination.
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The generalized method of moments is (almost) statistically efficient in low-SNR Gaussian latent-variable models
In low-SNR Gaussian latent-variable models, optimally weighted GMoM using minimal-order moments achieves the same leading asymptotic covariance as MLE via matching layerwise expansions of the information operators.