Paper Citation Record · LEDGER

Learning Fair Robustness via Domain Mixup

As of 13 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2411.14424.

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

pith.paper-citation-record.v1
2411.14424 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:18:04.174515Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact2
verified fuzzy9
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5675c2ee-6fbd-4e11-960e-ff306652de47 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Learning Fair Robustness via Domain Mixup Explaining and Harnessing Adversarial Examples

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.045431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bec68828-6f1f-4889-99c4-6757a111faa2 · outbound

This paper cites Intriguing properties of neural networks.

Learning Fair Robustness via Domain Mixup Intriguing properties of neural networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.051512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab8633e3-a99b-4a04-ba70-9d3bd4458943 · outbound

This paper cites Fooling a Real Car with Adversarial Traffic Signs.

Learning Fair Robustness via Domain Mixup Fooling a Real Car with Adversarial Traffic Signs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.056680Z

Source-reported events for the cited work

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Observation b28969c6-c3d8-4586-8c84-06dbab08ba21 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Learning Fair Robustness via Domain Mixup Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.061908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67bbb305-3a11-4192-816e-ba75d757f91d · outbound

This paper cites Boosting adversarial training with hypersphere embedding,.

Learning Fair Robustness via Domain Mixup Boosting adversarial training with hypersphere embedding,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.652958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f0f1a6e3-2649-4f11-bc14-973e53fb6093 · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

Learning Fair Robustness via Domain Mixup Theoretically principled trade-off between robustness and accuracy,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.072548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 393bd3d2-fe78-4057-aedd-791507d59cbc · outbound

This paper cites Unlabeled data improves adversarial robustness,.

Learning Fair Robustness via Domain Mixup Unlabeled data improves adversarial robustness,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.078440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b9e1805-5022-4c6a-9f21-2073175bec4a · outbound

This paper cites Adver- sarially robust generalization requires more data,.

Learning Fair Robustness via Domain Mixup Adver- sarially robust generalization requires more data,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.612706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ceecc4c3-052a-43bd-8d2b-db875463e458 · outbound

This paper cites Fast is better than free: Revisiting adversarial training.

Learning Fair Robustness via Domain Mixup Fast is better than free: Revisiting adversarial training

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.088367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8db5ef87-53a8-44fa-8f94-a3276d644d8b · outbound

This paper cites Provable tradeoffs in adversarially robust classification.

Learning Fair Robustness via Domain Mixup Provable tradeoffs in adversarially robust classification

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.094254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f74f613f-111d-455e-9cfb-9bd50169e373 · outbound

This paper cites Precise tradeoffs in adversarial training for linear regression,.

Learning Fair Robustness via Domain Mixup Precise tradeoffs in adversarial training for linear regression,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.593616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 164aea0c-0185-46bf-b1d2-0e4d4911f05d · outbound

This paper cites SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing.

Learning Fair Robustness via Domain Mixup SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.105440Z

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Observation 1e171a54-2a1d-4259-a6ae-273102e43500 · outbound

This paper cites Filtered Randomized Smoothing: A New Defense for Robust Modulation Classification.

Learning Fair Robustness via Domain Mixup Filtered Randomized Smoothing: A New Defense for Robust Modulation Classification

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:18:04.320296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 417006f2-695c-41a1-8019-638f768ce3c0 · outbound

This paper cites To be robust or to be fair: Towards fairness in adversarial training,.

Learning Fair Robustness via Domain Mixup To be robust or to be fair: Towards fairness in adversarial training,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.573596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 65b39828-c42c-493e-8a7d-598658ef3aaa · outbound

This paper cites Estimating and Improving Fairness with Adversarial Learning.

Learning Fair Robustness via Domain Mixup Estimating and Improving Fairness with Adversarial Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.123266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9033fe4b-cdf5-485b-8009-393f3b292aa9 · outbound

This paper cites Learning fair classifiers via min-max f- divergence regularization,.

Learning Fair Robustness via Domain Mixup Learning fair classifiers via min-max f- divergence regularization,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.556180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 651666ad-e2d0-43be-9db9-f5091343ce49 · outbound

This paper cites On the tradeoff between robustness and fairness,.

Learning Fair Robustness via Domain Mixup On the tradeoff between robustness and fairness,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.534701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8ea32c34-9bb1-4d6c-91a5-4ec7bbef98cf · outbound

This paper cites Robustness may be at odds with fairness: An empirical study on class-wise accuracy,.

Learning Fair Robustness via Domain Mixup Robustness may be at odds with fairness: An empirical study on class-wise accuracy,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.517487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 57f99f5a-a7f6-4d22-8cd5-f2f7466aa763 · outbound

This paper cites Fairness through robustness: Investigating robustness disparity in deep learning,.

Learning Fair Robustness via Domain Mixup Fairness through robustness: Investigating robustness disparity in deep learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.493052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7518b8fe-9f1b-4b51-8d0a-bf0cf69ff12a · outbound

This paper cites Intrinsic Fairness-Accuracy Tradeoffs under Equalized Odds.

Learning Fair Robustness via Domain Mixup Intrinsic Fairness-Accuracy Tradeoffs under Equalized Odds

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:18:04.274583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bbe74525-7229-433f-ac7a-54aa24d5f76f · outbound

This paper cites DAFA: Distance-Aware Fair Adversarial Training.

Learning Fair Robustness via Domain Mixup DAFA: Distance-Aware Fair Adversarial Training

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.157546Z

Source-reported events for the cited work

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Observation 1b12af5e-cbfc-4dcf-a785-37a3d912ddb4 · outbound

This paper cites mixup: Beyond empirical risk minimization,.

Learning Fair Robustness via Domain Mixup mixup: Beyond empirical risk minimization,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.164268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 428655b6-21ac-46ec-b26f-eba674513e43 · outbound

This paper cites How Does Mixup Help With Robustness and Generalization?.

Learning Fair Robustness via Domain Mixup How Does Mixup Help With Robustness and Generalization?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:04.169621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c39bd35b-83ad-4ea9-a534-da1b4a8fe875 · outbound

This paper cites Manifold mixup: Better representations by interpolat- ing hidden states,.

Learning Fair Robustness via Domain Mixup Manifold mixup: Better representations by interpolat- ing hidden states,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:18:04.459547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.