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Paper Citation Record · LEDGER

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models

As of 13 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2606.21873.

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pith.paper-citation-record.v1
2606.21873 v1

Coverage vector

measured 37 of 37 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-06-26T11:51:53.429491Z

measured 37 of 37 standing notices

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measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

37 of 37 outbound references displayed

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Outbound references

Observation b6c72cd9-8234-46a9-9592-02087e3682f2 · outbound

This paper cites An lp theory of pca and spectral clustering.The Annals of Statistics, 50(4):2359–2385, 2022.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models An lp theory of pca and spectral clustering.The Annals of Statistics, 50(4):2359–2385, 2022

Reference 1

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Observation d7723d2c-94a7-4530-8f71-fe7f9c3dddba · outbound

This paper cites Robust mean estimation under quantization.arXiv preprint arXiv:2601.07074, 2026.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Robust mean estimation under quantization.arXiv preprint arXiv:2601.07074, 2026

Reference 2

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Observation 18ffe3f7-f4cc-4e44-9529-8b1e50cc392b · outbound

This paper cites Courier Corporation, 2012.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Courier Corporation, 2012

Reference 3

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Observation badb986e-4bdd-4a05-90e1-ff20a42e9f9f · outbound

This paper cites Distributed adaptive gaussian mean estimation with unknown variance: Interactive protocol helps adaptation.The Annals of Statistics, 50(4):1992–2020, 2022.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Distributed adaptive gaussian mean estimation with unknown variance: Interactive protocol helps adaptation.The Annals of Statistics, 50(4):1992–2020, 2022

Reference 4

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Observation 62c0cadc-5b93-41bb-a836-407a002e9e0c · outbound

This paper cites A parameter-free two-bit covariance estimator with improved operator norm error rate.Applied and Computational Harmonic Analysis, page 101774, 2025.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models A parameter-free two-bit covariance estimator with improved operator norm error rate.Applied and Computational Harmonic Analysis, page 101774, 2025

Reference 5

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Observation b5c7b2a9-9193-4593-a01f-8f63a77bc8aa · outbound

This paper cites Ng, and Di Wang.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Ng, and Di Wang

Reference 6

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Observation c836fa47-829c-4a55-a30c-8b812938163e · outbound

This paper cites One-bit phase retrieval: Optimal rates and efficient algo- rithms.IEEE Transactions on Information Theory, 2026.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models One-bit phase retrieval: Optimal rates and efficient algo- rithms.IEEE Transactions on Information Theory, 2026

Reference 7

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Observation 57472b54-2204-44bf-b3b6-46b6ad683dd8 · outbound

This paper cites Cutoff for exact recovery of gaussian mixture models.IEEE Transactions on Information Theory, 67(6):4223–4238, 2021.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Cutoff for exact recovery of gaussian mixture models.IEEE Transactions on Information Theory, 67(6):4223–4238, 2021

Reference 8

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Observation c2c3f575-196f-40aa-8909-f5ef9b5728d7 · outbound

This paper cites Nonconvex optimization meets low-rank matrix factorization: An overview.IEEE Transactions on Signal Processing, 67(20):5239–5269, 2019.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Nonconvex optimization meets low-rank matrix factorization: An overview.IEEE Transactions on Signal Processing, 67(20):5239–5269, 2019

Reference 9

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Observation ae2a5a51-a479-4211-8d32-ed08fa53b4fb · outbound

This paper cites 1-bit matrix completion.Information and Inference: A Journal of the IMA, 3(3):189–223, 2014.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models 1-bit matrix completion.Information and Inference: A Journal of the IMA, 3(3):189–223, 2014

Reference 10

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Observation 40d5bb29-48c2-4843-8b8d-b66016e16e02 · outbound

This paper cites An overview of low-rank matrix recovery from incomplete observations.IEEE Journal of Selected Topics in Signal Processing, 10(4):608– 622, 2016.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models An overview of low-rank matrix recovery from incomplete observations.IEEE Journal of Selected Topics in Signal Processing, 10(4):608– 622, 2016

Reference 11

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Observation cc7763ba-3ef0-466d-8f39-60eaee55da86 · outbound

This paper cites Covariance estimation under one-bit quantization.The Annals of Statistics, 50(6):3538–3562, 2022.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Covariance estimation under one-bit quantization.The Annals of Statistics, 50(6):3538–3562, 2022

Reference 12

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Observation 7fd3e2ac-b303-4317-8046-4c3593cd3b87 · outbound

This paper cites Non-gaussian hyperplane tessellations and robust one-bit compressed sensing.Journal of the European Mathematical Society, 23(9):2913– 2947, 2021.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Non-gaussian hyperplane tessellations and robust one-bit compressed sensing.Journal of the European Mathematical Society, 23(9):2913– 2947, 2021

Reference 13

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Observation d598e69e-499d-4f16-9a1e-1ff47d8defab · outbound

This paper cites Phase retrieval by binary questions: Which complementary subspace is closer?Constructive Approximation, 56(1):1–33, 2022.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Phase retrieval by binary questions: Which complementary subspace is closer?Constructive Approximation, 56(1):1–33, 2022

Reference 14

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Observation a8cace13-832c-4e8c-84f1-75054554337f · outbound

This paper cites Hidden integrality of sdp relaxations for sub-gaussian mix- ture models.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Hidden integrality of sdp relaxations for sub-gaussian mix- ture models

Reference 15

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Observation 7d04d052-dd2f-4f35-b3c5-15cef3d049f5 · outbound

This paper cites Zhang, and Harrison H.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Zhang, and Harrison H

Reference 16

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Observation 8b16233f-4f82-4dda-9fa1-b481c568cffc · outbound

This paper cites Partial recovery bounds for clustering with the relaxedk-means.Mathematical Statistics and Learning, 1(3):317–374, 2019.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Partial recovery bounds for clustering with the relaxedk-means.Mathematical Statistics and Learning, 1(3):317–374, 2019

Reference 17

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Observation 89c0d8e7-a90c-4840-8cf1-48493113b01c · outbound

This paper cites Dithered quantizers.IEEE Transactions on Information Theory, 39(3):805–812, 1993.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Dithered quantizers.IEEE Transactions on Information Theory, 39(3):805–812, 1993

Reference 18

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Observation 447f3639-6fcc-4160-8bad-97ef77400b3e · outbound

This paper cites Robust 1- bit compressive sensing via binary stable embeddings of sparse vectors.IEEE Transactions on Information Theory, 59(4):2082–2102, 2013.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Robust 1- bit compressive sensing via binary stable embeddings of sparse vectors.IEEE Transactions on Information Theory, 59(4):2082–2102, 2013

Reference 19

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Observation 46c38361-c189-4761-bce7-c1e4403e4e4f · outbound

This paper cites Mean estimation from one-bit measurements.IEEE Transactions on Information Theory, 68(9):6276–6296, 2022.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Mean estimation from one-bit measurements.IEEE Transactions on Information Theory, 68(9):6276–6296, 2022

Reference 20

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Observation ac6c64a2-9f66-4b3a-a150-7d47f9f47e78 · outbound

This paper cites Optimality of spectral clus- tering in the gaussian mixture model.The Annals of Statistics, 49(5):2506–2530, 2021.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Optimality of spectral clus- tering in the gaussian mixture model.The Annals of Statistics, 49(5):2506–2530, 2021

Reference 21

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Observation 42f012c2-d624-4b14-98d5-21dd1cfe51ea · outbound

This paper cites Statistical and Computational Guarantees of Lloyd's Algorithm and its Variants.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Statistical and Computational Guarantees of Lloyd's Algorithm and its Variants

Reference 22

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Observation 452ab367-7f74-4d5d-a61f-50fd935b0f7a · outbound

This paper cites Sharp optimal recovery in the two component gaussian mixture model.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Sharp optimal recovery in the two component gaussian mixture model

Reference 23

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Observation 6bf58ba1-cd2c-4f25-b4b5-e1cf92114b08 · outbound

This paper cites Restricted strong convexity and weighted matrix completion: Optimal bounds with noise.Journal of Machine Learning Research, 13(1):1665–1697, 2012.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Restricted strong convexity and weighted matrix completion: Optimal bounds with noise.Journal of Machine Learning Research, 13(1):1665–1697, 2012

Reference 24

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Observation d1d6756a-e4cd-47bc-b4cb-202bddcc4d4a · outbound

This paper cites Approximating k-means-type clustering via semidefinite pro- gramming.SIAM journal on optimization, 18(1):186–205, 2007.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Approximating k-means-type clustering via semidefinite pro- gramming.SIAM journal on optimization, 18(1):186–205, 2007

Reference 25

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Observation 648dab1a-d099-4ed8-9c1e-1bd3d6a038e6 · outbound

This paper cites Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach.IEEE Transactions on Information Theory, 59(1):482–494, 2012.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach.IEEE Transactions on Information Theory, 59(1):482–494, 2012

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Observation bd504e3f-5d0f-472b-b5de-f440de56b2f1 · outbound

This paper cites Picture coding using pseudo-random noise.IRE Transactions on In- formation Theory, 8(2):145–154, 1962.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Picture coding using pseudo-random noise.IRE Transactions on In- formation Theory, 8(2):145–154, 1962

Reference 27

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Observation 4723e313-feb8-47bf-aab7-62701bc39183 · outbound

This paper cites Sketching for distributed deep learning: A sharper analysis.Advances in Neural Infor- mation Processing Systems, 37:6417–6447, 2024.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Sketching for distributed deep learning: A sharper analysis.Advances in Neural Infor- mation Processing Systems, 37:6417–6447, 2024

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Observation c16cf432-4ef0-470f-b9d9-f13fd404f632 · outbound

This paper cites The generalized lasso for sub-gaussian measurements with dithered quantization.IEEE Transactions on Information Theory, 66(4):2487–2500, 2020.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models The generalized lasso for sub-gaussian measurements with dithered quantization.IEEE Transactions on Information Theory, 66(4):2487–2500, 2020

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This paper cites Cambridge University Press, 2018.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Cambridge University Press, 2018

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Observation e225295e-e7a5-4353-888d-3fbf0e632ed6 · outbound

This paper cites Randomly initialized em algorithm for two-component gaussian mixture achieves near optimality ino( √n) iterations.Mathematical Statistics & Learning, 4, 2021.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Randomly initialized em algorithm for two-component gaussian mixture achieves near optimality ino( √n) iterations.Mathematical Statistics & Learning, 4, 2021

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Observation c3d37120-0aab-4d41-815a-eaad206cb082 · outbound

This paper cites Quantized compressive sensing with rip matrices: The benefit of dithering.Information and Inference: A Journal of the IMA, 9(3):543–586, 2020.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Quantized compressive sensing with rip matrices: The benefit of dithering.Information and Inference: A Journal of the IMA, 9(3):543–586, 2020

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Observation 225b178d-23ce-4091-a7bd-d32c6c5e020b · outbound

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One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Unresolved cited work

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Observation 4561dea3-cbf9-48a7-8666-b46ab97ead2b · outbound

This paper cites We lete i = ˜Xi−Xi fori∈[n], then we have thatei is zero-mean andO(λ) sub-Gaussian (cf.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models We lete i = ˜Xi−Xi fori∈[n], then we have thatei is zero-mean andO(λ) sub-Gaussian (cf

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This paper cites Recall that δ1 d=Q 2λ(θ+ε1 +τ1)−θ, 22 we shall introduce a surrogate ofδ1 as ˜δ1 :=λsign(θ+ε1 +τ1)−E [ λsign(θ+ε1 +τ1) ] = (˜δ11,···,˜δ1p)T.

One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Recall that δ1 d=Q 2λ(θ+ε1 +τ1)−θ, 22 we shall introduce a surrogate ofδ1 as ˜δ1 :=λsign(θ+ε1 +τ1)−E [ λsign(θ+ε1 +τ1) ] = (˜δ11,···,˜δ1p)T

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One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Unresolved cited work

Reference 36

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One-Bit Clustering for Two Component Sub-Gaussian Mixture Models Unresolved cited work

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