Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing | Paper Citation Record

Observation 14eed45a-2f05-4f1e-aa3e-9277e560210a · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Understanding intermediate layers using linear classifier probes

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This paper cites Self-Training: A Survey.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Self-Training: A Survey

Reference 2

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

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Reference 4

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Reference 5

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This paper cites InEuropean Conference on Computer Vision(2014).

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InEuropean Conference on Computer Vision(2014)

Reference 6

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This paper cites C., and Liang, P.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing C., and Liang, P

Reference 7

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Reference 8

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This paper cites Retraining with Predicted Hard Labels Provably Increases Model Accuracy.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Retraining with Predicted Hard Labels Provably Increases Model Accuracy

Reference 9

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This paper cites InInternational Conference on Machine Learning(2023), PMLR, pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InInternational Conference on Machine Learning(2023), PMLR, pp

Reference 10

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This paper cites Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Distillation $\approx$ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network

Reference 11

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Reference 12

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Observation 244551c9-4367-438b-a8b1-30ba9ae5526b · outbound

This paper cites L., Maleki, A., and Montanari, A.Message-passing algorithms for compressed sensing.Proceedings of the National Academy of Sciences 106, 45 (2009), 18914–18919.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing L., Maleki, A., and Montanari, A.Message-passing algorithms for compressed sensing.Proceedings of the National Academy of Sciences 106, 45 (2009), 18914–18919

Reference 13

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This paper cites Y., Venkataramanan, R., Rush, C., Samworth, R.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Y., Venkataramanan, R., Rush, C., Samworth, R

Reference 14

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This paper cites InInternational Conference on Machine Learning(2018), PMLR, pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InInternational Conference on Machine Learning(2018), PMLR, pp

Reference 15

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This paper cites PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels

Reference 16

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This paper cites InProceedings of the IEEE/CVF international conference on computer vision(2019), pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InProceedings of the IEEE/CVF international conference on computer vision(2019), pp

Reference 17

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 18

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 19

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This paper cites 1: Distribution theory.London [etc.]: Arnold [etc.](1994).

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing 1: Distribution theory.London [etc.]: Arnold [etc.](1994)

Reference 20

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This paper cites InInternational conference on machine learning(2020), PMLR, pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InInternational conference on machine learning(2020), PMLR, pp

Reference 21

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This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 22

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This paper cites InWorkshop on challenges in representation learning, ICML (2013), vol.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InWorkshop on challenges in representation learning, ICML (2013), vol

Reference 23

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This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 24

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This paper cites InProceedings of the IEEE International Conference on Computer Vision (2017), pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InProceedings of the IEEE International Conference on Computer Vision (2017), pp

Reference 25

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 26

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This paper cites InInternational Conference on Artificial Intelligence and Statistics(2021), PMLR, pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InInternational Conference on Artificial Intelligence and Statistics(2021), PMLR, pp

Reference 27

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 28

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 29

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 30

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This paper cites SELF: Learning to Filter Noisy Labels with Self-Ensembling.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 31

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This paper cites Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training

Reference 32

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This paper cites Understanding the Gains from Repeated Self-Distillation.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Understanding the Gains from Repeated Self-Distillation

Reference 33

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This paper cites S., Bandeira, A.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing S., Bandeira, A

Reference 34

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This paper cites Understanding and Mitigating the Tradeoff Between Robustness and Accuracy.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Understanding and Mitigating the Tradeoff Between Robustness and Accuracy

Reference 35

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This paper cites In2011 IEEE International Symposium on Information Theory Proceedings(2011), IEEE, pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing In2011 IEEE International Symposium on Information Theory Proceedings(2011), IEEE, pp

Reference 36

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This paper cites K.Iterative estimation of constrained rank-one matrices in noise.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing K.Iterative estimation of constrained rank-one matrices in noise

Reference 37

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This paper cites Training Deep Neural Networks on Noisy Labels with Bootstrapping.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Training Deep Neural Networks on Noisy Labels with Bootstrapping

Reference 38

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 39

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 40

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This paper cites InProceedings of the IEEE conference on computer vision and pattern recognition(2018), pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing InProceedings of the IEEE conference on computer vision and pattern recognition(2018), pp

Reference 41

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This paper cites Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 42

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Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Unresolved cited work

Reference 43

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This paper cites In 33rd annual meeting of the association for computational linguistics(1995), pp.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing In 33rd annual meeting of the association for computational linguistics(1995), pp

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:19.607933Z

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This paper cites How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis

Reference 45

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This paper cites Hence, the first step strictly reduces the test error and the next rounds of retraining do not increase the test error.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Hence, the first step strictly reduces the test error and the next rounds of retraining do not increase the test error

Reference 46

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This paper cites To this end, we derive a lower bound onF, such that ˜F(u)<F(u) , ∀u≥ 0, and establish condition on the label flipping probabilityp, so that η2 1 ≤ ˜F(η 2.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing To this end, we derive a lower bound onF, such that ˜F(u)<F(u) , ∀u≥ 0, and establish condition on the label flipping probabilityp, so that η2 1 ≤ ˜F(η 2

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:19.162642Z

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This paper cites 17 To construct ˜F, recall that˜gis the Bayes-optimal aggregator given by˜g(˜Y ,̂Y)=E[Y∣ ˜Y= ¯ηY+√¯ηG,̂Y].

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing 17 To construct ˜F, recall that˜gis the Bayes-optimal aggregator given by˜g(˜Y ,̂Y)=E[Y∣ ˜Y= ¯ηY+√¯ηG,̂Y]

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:18.905187Z

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This paper cites These properties give a clear picture of the function h: it will be positive on[0, p∗), negative on(p∗, 1.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing These properties give a clear picture of the function h: it will be positive on[0, p∗), negative on(p∗, 1

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:18.681334Z

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This paper cites Hence, condition(32), i.e.,h(p)≤0holds ifp∈[p ∗, 1 2), which completes the proof.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Hence, condition(32), i.e.,h(p)≤0holds ifp∈[p ∗, 1 2), which completes the proof

Reference 50

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This paper cites The test set consists of 624 examples.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing The test set consists of 624 examples

Reference 51

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Observation daabf98b-d982-4cac-a1a4-7ffea1861e44 · outbound

This paper cites Ramen( https://www.tensorflow.org/datasets/catalog/food101): Each class in Food-101 has 750 training examples; so the total number of examples for the two classes is 1500.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Ramen( https://www.tensorflow.org/datasets/catalog/food101): Each class in Food-101 has 750 training examples; so the total number of examples for the two classes is 1500

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:32:18.060509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers