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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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9 results for “gradient-EM”

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixturescs.LG

Gradient EM converges globally for over-parameterized Gaussian mixtures.

problem Recovering ground truth Gaussian mixtures with over-parameterized models.

method Gradient EM with over-parameterization, using Hermite polynomials and tensor decomposition.

result Gradient EM globally converges to ground truth with

n = Ω(m\log m)

over-parameterization.

Gaussian mixtures Expectation-Maximization Gradient EM Global convergence Over-parameterization

2025-06-06abs ↗pdf ↗

Toward Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixture Modelscs.LG

Gradient EM converges globally for over-parameterized Gaussian mixtures.

problem Global convergence of gradient EM for Gaussian mixtures with more than 2 components.

method Likelihood-based convergence analysis framework.

result Gradient EM converges globally with a sublinear rate of O(1/√t).

Gaussian Mixture Models Gradient EM Over-parameterized Global Convergence Sublinear Rate

2024-06-29abs ↗pdf ↗

Gradient-EM Bayesian Meta-learningcs.LG

Gradient-EM Bayesian meta-learning accelerates adaptation with reduced computation and improved robustness.

problem Efficient and robust adaptation to new tasks with uncertainty assessment.

method Extends Bayesian meta-learning with gradient-EM algorithm, decoupling inner-update from meta-update.

result Improves accuracy with less computation cost and enhanced robustness to uncertainty.

Bayesian meta-learning gradient-EM deep neural networks few-shot learning reinforcement learning

2020-06-21abs ↗pdf ↗

Expectation Maximization (EM) Converges for General Agnostic Mixturescs.LG

Gradient EM converges exponentially to optimal solution in agnostic mixtures.

problem Fitting

k

parametric functions to given data points without a generative model.

method Gradient EM algorithm for agnostic mixtures of arbitrary parametric functions.

result Gradient EM converges exponentially to population loss minimizers with high probability.

Gradient EM agnostic mixtures parametric functions exponential convergence optimal solution

2026-04-07abs ↗pdf ↗

Statistical guarantees for the EM algorithm: From population to sample-based analysismath.ST

We develop a general framework for proving rigorous guarantees on the performance of the EM algorithm and a variant known as gradient EM. Our analysis is divided into two parts: a treatment of these algorithms at the population level (in the limit of infinite data), followed by results that apply to updates based on a …

2014-08-09abs ↗pdf ↗

Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guaranteescs.LG

New DP EM algorithm with statistical guarantees for mixture models.

problem Preserving privacy in EM algorithms for mixture models.

method Proposed a DP EM algorithm with statistical guarantees.

result Near optimal estimation error for GMM in DP model.

Differentially Private Expectation Maximization Mixture Models Statistical Guarantees Privacy Preserving Algorithms

2020-10-22abs ↗pdf ↗

Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithmsstat.ML

This paper develops a federated EM algorithm for unsupervised learning of mixture models.

problem Theoretical foundations of unsupervised federated learning are lacking.

method Introduces a federated gradient EM algorithm (FedGrEM) for unsupervised learning of mixture models.

result Theoretical analysis shows FedGrEM outperforms local single-task learning.

federated learning unsupervised learning federated EM mixture models finite-sample theory

2023-10-23abs ↗pdf ↗

Bayesian Meta-reinforcement Learning for Traffic Signal Controlcs.LG

Novel Bayesian meta-reinforcement learning framework improves traffic signal control robustness.

problem Lack of robustness and stability in adaptation for traffic signal control.

method Value-based Bayesian meta-reinforcement learning framework BM-DQN with fast-adaptation variation and DQN fast-update advantage.

result Framework adapts more quickly and robustly to new scenarios than previous methods.

Bayesian meta-reinforcement learning traffic signal control robustness continual learning heterogeneous scenarios

2020-10-01abs ↗pdf ↗

Local linear convergence of gradient methods for overparameterized Gaussian mixturescs.LG

Gradient method converges locally linearly for overparameterized Gaussian mixtures.

problem Learning Gaussian mixtures under overparameterization.

method Gradient-based method alternating short descent steps and long Polyak steps.

result Gradient method converges locally linearly to minimizers.

Gaussian mixtures overparameterization gradient descent local linear convergence Polyak stepsize

2026-05-29abs ↗pdf ↗