Paper Citation Record · LEDGER

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization

As of 16 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2412.05169.

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

pith.paper-citation-record.v1
2412.05169 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:57:20.333112Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T02:35:39.845487Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T11:56:56.059724Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 53d52d20-9b84-4975-aa58-2c6d5091879b · outbound

This paper cites Sharpness-aware minimization leads to low-rank features.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Sharpness-aware minimization leads to low-rank features

Reference 1

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Observation cd3c0d11-c818-4154-a6f6-bc1e3711e1ca · outbound

This paper cites Invariant risk minimization, 2020.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Invariant risk minimization, 2020

Reference 2

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Observation 326dcb55-61eb-448d-bd35-3df90565700b · outbound

This paper cites Why is sam robust to label noise? In International Conference on Learning Representations (ICLR), 2024.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Why is sam robust to label noise? In International Conference on Learning Representations (ICLR), 2024

Reference 3

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Observation cef66bce-7442-4f02-8686-8483c30fdf7b · outbound

This paper cites A theory of learning from different domains.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization A theory of learning from different domains

Reference 4

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Observation bfd2176b-a834-4e3b-8d48-faee902ba10a · outbound

This paper cites Blackard and Denis J.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Blackard and Denis J

Reference 5

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Observation f59bb4a3-51aa-4af8-b3b7-a54bb4170414 · outbound

This paper cites Optimization methods for large-scale machine learning.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Optimization methods for large-scale machine learning

Reference 6

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Source-reported events for the cited work

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Observation e4cfda1f-0013-441a-9331-6e90097b9ec8 · outbound

This paper cites Swad: Domain generalization by seeking flat minima.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Swad: Domain generalization by seeking flat minima

Reference 7

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Observation 3323ed48-6f9d-4c4b-873f-8e08938fa996 · outbound

This paper cites Entropy-sgd: Biasing gradient descent into wide valleys.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Entropy-sgd: Biasing gradient descent into wide valleys

Reference 8

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Observation 1bb1305b-c76e-46f4-8d73-83cfe3ed0f73 · outbound

This paper cites Why does sharpness-aware minimization generalize better than sgd? Advances in neural information processing systems, 36, 2024.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Why does sharpness-aware minimization generalize better than sgd? Advances in neural information processing systems, 36, 2024

Reference 9

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Observation 56f2178b-ecc2-452f-ab94-8c198ec91200 · outbound

This paper cites Sharp minima can generalize for deep nets.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Sharp minima can generalize for deep nets

Reference 10

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Observation e804b8f4-de11-4a62-913e-ae4ffc1ae4dd · outbound

This paper cites Efficient sharpness-aware minimization for improved training of neural networks.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Efficient sharpness-aware minimization for improved training of neural networks

Reference 11

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This paper cites Sharpness-aware minimization for efficiently improving generalization.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Sharpness-aware minimization for efficiently improving generalization

Reference 12

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This paper cites Sachs, Brian Yin, Crystal Lee, Philipp Krähenbühl, and Alexei A.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Sachs, Brian Yin, Crystal Lee, Philipp Krähenbühl, and Alexei A

Reference 13

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Observation 32d86d00-2373-47ef-9102-b77c04fc4bd0 · outbound

This paper cites Gradual domain adaptation: Theory and algorithms.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Gradual domain adaptation: Theory and algorithms

Reference 14

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Observation 8b28a5c8-02a6-463b-8316-608541f4ae8b · outbound

This paper cites Flat Minima.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Flat Minima

Reference 15

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Observation eba6f2b8-4817-4264-ab84-51fd29dc21f2 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 16

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Observation 8068fa23-5c1d-4d43-9543-78bc266db910 · outbound

This paper cites Averaging weights leads to wider optima and better generalization.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Averaging weights leads to wider optima and better generalization

Reference 17

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Observation ad513d2d-8950-48d2-af93-21a1ef5f2352 · outbound

This paper cites Fantastic generalization measures and where to find them, 2020.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Fantastic generalization measures and where to find them, 2020

Reference 18

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This paper cites When do flat minima optimizers work? Advances in Neural Information Processing Systems, 35: 0 16577--16595, 2022.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization When do flat minima optimizers work? Advances in Neural Information Processing Systems, 35: 0 16577--16595, 2022

Reference 19

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This paper cites On large-batch training for deep learning: Generalization gap and sharp minima.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization On large-batch training for deep learning: Generalization gap and sharp minima

Reference 20

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Observation d1a295b6-526b-4f72-a5f1-7d3a84d81d86 · outbound

This paper cites Fast and scalable bayesian deep learning by weight-perturbation in adam.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Fast and scalable bayesian deep learning by weight-perturbation in adam

Reference 21

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Observation e3dd3c6d-6e74-45ee-96d9-a59b8ca70b0b · outbound

This paper cites F isher SAM : Information geometry and sharpness aware minimisation.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization F isher SAM : Information geometry and sharpness aware minimisation

Reference 22

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Observation 0d041e57-4433-4c12-9548-5bdb7bb44a5c · outbound

This paper cites Understanding self-training for gradual domain adaptation.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Understanding self-training for gradual domain adaptation

Reference 23

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This paper cites Kuznetsov and M.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Kuznetsov and M

Reference 24

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This paper cites Discrepancy-based theory and algorithms for forecasting non-stationary time series, 2020 b.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Discrepancy-based theory and algorithms for forecasting non-stationary time series, 2020 b

Reference 25

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This paper cites Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Asam: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks

Reference 26

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This paper cites The mnist database of handwritten digits.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization The mnist database of handwritten digits

Reference 27

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Observation c48f1f17-24d1-446d-a2e4-e5258e0f3a74 · outbound

This paper cites Friendly sharpness-aware minimization.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Friendly sharpness-aware minimization

Reference 28

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This paper cites Fisher-rao metric, geometry, and complexity of neural networks.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Fisher-rao metric, geometry, and complexity of neural networks

Reference 29

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This paper cites Towards efficient and scalable sharpness-aware minimization.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Towards efficient and scalable sharpness-aware minimization

Reference 30

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This paper cites New insights and perspectives on the natural gradient method.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization New insights and perspectives on the natural gradient method

Reference 31

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This paper cites Normalization layers are all that sharpness-aware minimization needs.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Normalization layers are all that sharpness-aware minimization needs

Reference 32

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This paper cites K-SAM: Sharpness-Aware Minimization at the Speed of SGD.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization K-SAM: Sharpness-Aware Minimization at the Speed of SGD

Reference 33

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Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Online learning via sequential complexities

Reference 34

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Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Unresolved cited work

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This paper cites Sharpness-aware minimization enhances feature quality via balanced learning.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Sharpness-aware minimization enhances feature quality via balanced learning

Reference 36

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Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Dropout: A simple way to prevent neural networks from overfitting

Reference 37

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This paper cites Understanding gradual domain adaptation: Improved analysis, optimal path and beyond.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Understanding gradual domain adaptation: Improved analysis, optimal path and beyond

Reference 38

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Observation 2b70935b-95f2-47ac-aba0-ee2e3e783a51 · outbound

This paper cites Sharpness minimization algorithms do not only minimize sharpness to achieve better generalization.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Sharpness minimization algorithms do not only minimize sharpness to achieve better generalization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:57:20.593099Z

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This paper cites Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Reference 40

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This paper cites Towards a theoretical framework of out-of-distribution generalization.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Towards a theoretical framework of out-of-distribution generalization

Reference 41

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This paper cites Flatness-aware minimization for domain generalization.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Flatness-aware minimization for domain generalization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:57:20.559775Z

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Observation 2cbe782e-8c93-49e7-a568-2078acdf5488 · outbound

This paper cites On learning invariant representations for domain adaptation.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization On learning invariant representations for domain adaptation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:57:20.546149Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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This paper cites Fundamental limits and tradeoffs in invariant representation learning.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Fundamental limits and tradeoffs in invariant representation learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:57:20.532010Z

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Observation 41f2b8f6-5858-42b1-bd7c-87f0cf08efbb · outbound

This paper cites Gradual domain adaptation via gradient flow.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Gradual domain adaptation via gradient flow

Reference 45

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Observation 150b1a79-28e1-4ad4-927c-51c3d77728e8 · outbound

This paper cites Towards robust out-of-distribution generalization bounds via sharpness.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization Towards robust out-of-distribution generalization bounds via sharpness

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:57:20.502650Z

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This paper cites write newline.

Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization write newline

Reference 47

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Pith citing papers

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Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings cites this paper.

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:30:54.169150Z

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Observation 49b96ddb-2e88-4616-b950-5deefbf562d3 · inbound

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss cites this paper.

Double Preconditioning (DoPr): Optimization for Test-Time Performance, not Validation Loss Towards Understanding the Role of Sharpness-Aware Minimization Algorithms for Out-of-Distribution Generalization

Reference 188

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