Paper Citation Record · LEDGER

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures

As of 17 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 2 inbound Pith citation observations for arXiv:2506.06584.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.06584 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:07.556292Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T23:40:46.820775Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T03:29:30.804420Z

Reference resolution

48 of 48 outbound references displayed

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External citation measurements

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Outbound references

Observation 824ae9c9-2fc9-4dea-84c4-18802d551d3d · outbound

This paper cites an unresolved cited work.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Unresolved cited work

Reference 1

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Observation a086df68-1b8d-449c-bed4-1473a109c427 · outbound

This paper cites Kakade, and Matus Telgarsky.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Kakade, and Matus Telgarsky

Reference 2

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Observation b5544151-21dd-4a03-b5ca-756c05eff304 · outbound

This paper cites Learning time-scales in two-layers neural networks.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Learning time-scales in two-layers neural networks

Reference 3

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Observation a4998754-753e-4d43-a920-baff9a346197 · outbound

This paper cites Stochastic approximation: a dynamical systems viewpoint , volume 9.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Stochastic approximation: a dynamical systems viewpoint , volume 9

Reference 4

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Observation 2c071eb5-c3ce-4474-adad-e0b67ce530f9 · outbound

This paper cites Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime

Reference 5

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Observation bf441ac8-d070-4847-bf09-8d23ce55d731 · outbound

This paper cites Mirror descent and nonlinear projected subgradient methods for convex optimization.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Mirror descent and nonlinear projected subgradient methods for convex optimization

Reference 6

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Observation 59554b2e-f724-4cdc-a275-f905f4a4aff0 · outbound

This paper cites Wainwright, and Bin Yu.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Wainwright, and Bin Yu

Reference 7

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Observation 6005686a-acd7-49d8-9283-21bfc6628219 · outbound

This paper cites Extreme ratio between spectral and frobenius norms of nonnegative tensors.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Extreme ratio between spectral and frobenius norms of nonnegative tensors

Reference 8

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Observation a4b87d9f-ef0b-4dea-bd31-a094a4f1dbb2 · outbound

This paper cites Local minima structures in gaussian mixture models.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Local minima structures in gaussian mixture models

Reference 9

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Observation 7f9f9602-da01-43b8-95f4-f5a97bcb46e5 · outbound

This paper cites Learning mixtures of gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Learning mixtures of gaussians

Reference 10

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Observation 58cc5545-eb44-4cf3-8bf9-405ce5e85cbc · outbound

This paper cites Singularity, misspecification and the convergence rate of EM.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Singularity, misspecification and the convergence rate of EM

Reference 11

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Observation 1b953d37-589f-44aa-9af9-3ab1161b59a6 · outbound

This paper cites Wainwright, and Michael I.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Wainwright, and Michael I

Reference 12

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Observation c1d5f879-5eff-40e2-a72f-f263363d4de5 · outbound

This paper cites Wainwright, Michael I.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Wainwright, Michael I

Reference 13

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Observation 6091336c-0796-40a1-87f9-4e67f084f6fb · outbound

This paper cites Maximum likelihood from incomplete data via the em algorithm.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Maximum likelihood from incomplete data via the em algorithm

Reference 14

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Observation a384c81f-1ff1-43e9-a43f-227828925dc8 · outbound

This paper cites Schulman.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Schulman

Reference 15

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Observation 97742c8e-64f2-43a6-b6e8-0e4c88a3b424 · outbound

This paper cites Ten steps of EM suffice for mixtures of two gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Ten steps of EM suffice for mixtures of two gaussians

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:16.066234Z

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Observation bcbac740-ab5f-48fb-9efd-c7bb26c7d5d0 · outbound

This paper cites A kernel two-sample test.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures A kernel two-sample test

Reference 17

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Observation b1e6a91a-7377-4248-8564-3c2c68a5640e · outbound

This paper cites Learning mixtures of gaussians in high dimensions.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Learning mixtures of gaussians in high dimensions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:15.584621Z

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Observation 4b7dcbf0-bd09-43c1-98d9-f760d8868f93 · outbound

This paper cites Learning mixtures of spherical gaussians: moment methods and spectral decompositions.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Learning mixtures of spherical gaussians: moment methods and spectral decompositions

Reference 19

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Observation 295140d0-7d05-4711-aa0b-4aab946bd768 · outbound

This paper cites Mixture models, robustness, and sum of squares proofs.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Mixture models, robustness, and sum of squares proofs

Reference 20

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Observation 641cdf8f-8600-419e-8be9-0c40beb9f40d · outbound

This paper cites Revisiting frank-wolfe: Projection-free sparse convex optimization.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Revisiting frank-wolfe: Projection-free sparse convex optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:15.091537Z

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Observation 4a03c832-88a3-4837-a40d-f3baad60343e · outbound

This paper cites Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Local maxima in the likelihood of gaussian mixture models: Structural results and algorithmic consequences

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:14.795871Z

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Observation 476a9491-c384-498b-95eb-f0d2d6129dfc · outbound

This paper cites Robust learning of mixtures of gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Robust learning of mixtures of gaussians

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:14.599583Z

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Observation 71692856-5012-4a81-bbf2-121238f8fc78 · outbound

This paper cites The EM algorithm gives sample-optimality for learning mixtures of well-separated gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures The EM algorithm gives sample-optimality for learning mixtures of well-separated gaussians

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:14.360664Z

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Observation 92d3baa4-9f38-4c98-920a-a5543db1cb49 · outbound

This paper cites Efficiently learning mixtures of two gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Efficiently learning mixtures of two gaussians

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:14.140708Z

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Observation e1b90d5d-c4b8-48d8-be59-610e81538c45 · outbound

This paper cites Robust moment estimation and improved clustering via sum of squares.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Robust moment estimation and improved clustering via sum of squares

Reference 26

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Observation 3a33be04-7278-4ff3-aced-1874d935f8be · outbound

This paper cites Homotopy analysis method in nonlinear differential equations , volume 153.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Homotopy analysis method in nonlinear differential equations , volume 153

Reference 27

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a9adb427-a478-449f-aa84-56475759ae90 · outbound

This paper cites Clustering mixtures with almost optimal separation in polynomial time.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Clustering mixtures with almost optimal separation in polynomial time

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:13.581681Z

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Observation d908b2f1-40c0-4aa1-9e93-004560b3b991 · outbound

This paper cites Robustly learning general mixtures of gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Robustly learning general mixtures of gaussians

Reference 29

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Observation f0888ada-818d-4f23-b22d-9c47e7c71cf7 · outbound

This paper cites On orthogonal tensors and best rank-one approximation ratio.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On orthogonal tensors and best rank-one approximation ratio

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:13.070809Z

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Observation 968b8d70-d496-4790-bc50-c8db3cf80089 · outbound

This paper cites Leveraging the two-timescale regime to demonstrate convergence of neural networks.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Leveraging the two-timescale regime to demonstrate convergence of neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:12.748771Z

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Observation c9b9b18a-5745-43d7-bcf8-d3a1e2bde9fe · outbound

This paper cites On relations between the relative entropy and 2-divergence, generalizations and applications.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On relations between the relative entropy and 2-divergence, generalizations and applications

Reference 32

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Observation 9b31119f-31af-4fc0-a7a2-18591464cbb8 · outbound

This paper cites Primer on monotone operator methods.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Primer on monotone operator methods

Reference 33

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Observation 7bc2f3a0-a86b-45f5-a8a5-16b4f379dc71 · outbound

This paper cites On learning mixtures of well-separated gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On learning mixtures of well-separated gaussians

Reference 34

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Observation 97313328-7255-43a7-8da3-d178a5149b35 · outbound

This paper cites Improved convergence guarantees for learning gaussian mixture models by em and gradient em.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Improved convergence guarantees for learning gaussian mixture models by em and gradient em

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:11.464093Z

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Observation 06b61067-21f9-44b6-aecd-856c68fb917d · outbound

This paper cites Estimation of the mean of a multivariate normal distribution.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Estimation of the mean of a multivariate normal distribution

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:11.199385Z

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Observation ab3c40d5-6c23-4b72-a861-50b7dc97cd8b · outbound

This paper cites Mean-field analysis on two-layer neural networks from a kernel perspective.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Mean-field analysis on two-layer neural networks from a kernel perspective

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:10.878067Z

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Observation e0ad55d3-9617-428c-91c4-7fc697b0d19f · outbound

This paper cites The EM algorithm is adaptively-optimal for unbalanced symmetric gaussian mixtures.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures The EM algorithm is adaptively-optimal for unbalanced symmetric gaussian mixtures

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:10.636896Z

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Observation b8dafbd1-4c62-49bd-ac0a-c533dd0777de · outbound

This paper cites On the convergence properties of the em algorithm.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On the convergence properties of the em algorithm

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:10.350087Z

Source-reported events for the cited work

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Observation 7f0895c1-8de6-45f3-8710-d7d312b89a83 · outbound

This paper cites Randomly initialized EM algorithm for two-component gaussian mixture achieves near optimality in o( n ) iterations.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Randomly initialized EM algorithm for two-component gaussian mixture achieves near optimality in o( n ) iterations

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:10.064279Z

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Observation 8da91c6c-1c49-4746-9745-9a50d90272b4 · outbound

This paper cites Over-parameterization exponentially slows down gradient descent for learning a single neuron.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Over-parameterization exponentially slows down gradient descent for learning a single neuron

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:09.782385Z

Source-reported events for the cited work

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Observation 81d64e69-5931-4bf4-a476-e76b7a2cc81b · outbound

This paper cites Toward global convergence of gradient EM for over-paramterized gaussian mixture models.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Toward global convergence of gradient EM for over-paramterized gaussian mixture models

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:09.492336Z

Source-reported events for the cited work

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Observation cc660863-0db0-410d-8cb9-ee96c2f9af71 · outbound

This paper cites Hsu, and Arian Maleki.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Hsu, and Arian Maleki

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:09.218392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 77da29f8-8043-491c-897a-d93d7cb615ab · outbound

This paper cites On convergence properties of the EM algorithm for gaussian mixtures.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures On convergence properties of the EM algorithm for gaussian mixtures

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:08.973383Z

Source-reported events for the cited work

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Observation 61a888bc-77a4-496c-98a6-3d73ab22dff2 · outbound

This paper cites Convergence of gradient em on multi-component mixture of gaussians.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Convergence of gradient em on multi-component mixture of gaussians

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:08.706725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation debc93fa-0b87-4722-9441-590d76613bfe · outbound

This paper cites How does gradient descent learn features --- a local analysis for regularized two-layer neural networks.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures How does gradient descent learn features --- a local analysis for regularized two-layer neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:08.406989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8135a29b-2a7f-4451-a803-a0afd5d30dd5 · outbound

This paper cites A local convergence theory for mildly over-parameterized two-layer neural network.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures A local convergence theory for mildly over-parameterized two-layer neural network

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:08.089498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae1f0ac5-35c5-4a5f-91c1-fb2d34b670c2 · outbound

This paper cites Statistical convergence of the em algorithm on gaussian mixture models.

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures Statistical convergence of the em algorithm on gaussian mixture models

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:07.845037Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation ee04510f-e9f9-4d94-a4da-d433b2afe4aa · inbound

Local linear convergence of gradient methods for overparameterized Gaussian mixtures cites this paper.

Local linear convergence of gradient methods for overparameterized Gaussian mixtures Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:42:49.405457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fc09f6a2-dbb4-4a6b-ae9e-176cf59fcc0d · inbound

Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence cites this paper.

Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:29:30.806812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.