Paper Citation Record · LEDGER

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation

As of 20 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2506.17874.

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

pith.paper-citation-record.v1
2506.17874 v2

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:06:49.574362Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T14:05:30.109141Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:13:30.293874Z

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy16
unresolved22
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malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b2f9429-8d6a-4be6-b4fc-6f7c1e7ff44b · outbound

This paper cites cambridge university press, 2009.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation cambridge university press, 2009

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.393136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05deb867-cd5e-4d42-a7c9-5fee8447f21c · outbound

This paper cites Wasserstein distributional robustness of neural networks, 2023.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Wasserstein distributional robustness of neural networks, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.283753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d9a40109-6958-4ce7-9352-a91191d832ac · outbound

This paper cites Sensitivity analysis of Wasserstein distributionally robust optimization problems.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Sensitivity analysis of Wasserstein distributionally robust optimization problems

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T19:06:49.907589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ceeb9c1b-749c-45c7-aef1-65c8d83e5907 · outbound

This paper cites Almost linear vc dimension bounds for piecewise polynomial networks.Advances in neural information processing systems, 11, 1998.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Almost linear vc dimension bounds for piecewise polynomial networks.Advances in neural information processing systems, 11, 1998

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.268962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4fd3c7ad-d453-41b1-81fd-949c445b33a5 · outbound

This paper cites Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.254188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 37501ffa-0303-4ee8-9319-87f8fd6ec49b · outbound

This paper cites Deep neural networks for nonparametric interaction models with diverging dimension.The Annals of Statistics, 52(6):2738–2766, 2024.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Deep neural networks for nonparametric interaction models with diverging dimension.The Annals of Statistics, 52(6):2738–2766, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.237395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9909e6d-7e60-4e46-8fe9-2752d0232788 · outbound

This paper cites Multivariate distributionally robust convex regression under absolute error loss.Advances in Neural Information Processing Systems, 32, 2019.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Multivariate distributionally robust convex regression under absolute error loss.Advances in Neural Information Processing Systems, 32, 2019

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.217629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b48b35d9-b19d-4116-b972-baba337ce070 · outbound

This paper cites Robust wasserstein profile inference and applications to machine learning.Journal of Applied Probability, 56(3):830–857, 2019.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Robust wasserstein profile inference and applications to machine learning.Journal of Applied Probability, 56(3):830–857, 2019

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.201880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d7bb405b-4f4b-4549-8101-c166708edff4 · outbound

This paper cites Confidence regions in wasserstein distributionally robust estimation.Biometrika, 109(2):295–315, 2022.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Confidence regions in wasserstein distributionally robust estimation.Biometrika, 109(2):295–315, 2022

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.186130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a25ea303-b7ad-4c43-abfb-6521eaa8b14b · outbound

This paper cites Listen, attend and spell: A neural network for large vocabulary conversational speech recognition.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Listen, attend and spell: A neural network for large vocabulary conversational speech recognition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.170535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 18b7ffed-8fbb-4706-b417-563a61f091a0 · outbound

This paper cites Distributionally robust multiclass classification and applications in deep cnn image classifiers.stat, 1050:27, 2021.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Distributionally robust multiclass classification and applications in deep cnn image classifiers.stat, 1050:27, 2021

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.156724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 90b3e7bf-9864-4554-9fe3-cca8d64cfbae · outbound

This paper cites A robust learning approach for regression models based on distributionally robust optimization.Journal of Machine Learning Research, 19(13):1–48, 2018.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation A robust learning approach for regression models based on distributionally robust optimization.Journal of Machine Learning Research, 19(13):1–48, 2018

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.449751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06250727-ff7e-4a1b-8c39-6829565fea8f · outbound

This paper cites Distributionally robust optimization under moment uncertainty with application to data-driven problems.Operations research, 58(3):595–612, 2010.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Distributionally robust optimization under moment uncertainty with application to data-driven problems.Operations research, 58(3):595–612, 2010

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.454377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d2a6f40-414b-44e3-8dab-3f65cacf4821 · outbound

This paper cites Bert: Pre-training of deep bidi- rectional transformers for language understanding.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Bert: Pre-training of deep bidi- rectional transformers for language understanding

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.458749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93d71caa-6507-4228-9fc6-5e10aa8f99f5 · outbound

This paper cites NoisyMix: Boosting Model Robustness to Common Corruptions.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation NoisyMix: Boosting Model Robustness to Common Corruptions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.463411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c68aeb3d-a800-4843-9373-6200f56406b2 · outbound

This paper cites Wasserstein distributionally robust optimization and variation regularization.Operations Research, 72(3):1177–1191, 2024.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Wasserstein distributionally robust optimization and variation regularization.Operations Research, 72(3):1177–1191, 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.468246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7efca5d8-3f97-4147-9741-67773d8657c1 · outbound

This paper cites Motivating the Rules of the Game for Adversarial Example Research.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Motivating the Rules of the Game for Adversarial Example Research

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.472455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d322ae7-0d1a-47b8-b277-27151c1d4740 · outbound

This paper cites Deep residual learning for image recognition.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Deep residual learning for image recognition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.477147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd34912b-5465-4197-a3d5-68d73d703161 · outbound

This paper cites Identity mappings in deep residual networks.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Identity mappings in deep residual networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.092532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 30102c67-9e5a-459f-b3d2-608e98091ff0 · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.485268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d096ea3d-b858-488d-b612-d5cf5bffc5cb · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.489738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4700f218-cc19-4a34-8bf8-d22cacfa6e32 · outbound

This paper cites Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.IEEE Signal processing magazine, 29(6):82–97, 2012.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.IEEE Signal processing magazine, 29(6):82–97, 2012

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.076797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 10b94cf1-b946-4ccf-80ab-a883f913accb · outbound

This paper cites Adversarial classification via distributional robustness with wasserstein ambiguity.Mathematical Programming, 198(2):1411–1447, 2023.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Adversarial classification via distributional robustness with wasserstein ambiguity.Mathematical Programming, 198(2):1411–1447, 2023

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.060993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1581ec5-4878-4c45-8b4a-2f6b61991348 · outbound

This paper cites On the rate of convergence of fully connected deep neural network regression estimates.The Annals of Statistics, 49(4):2231–2249, 2021.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation On the rate of convergence of fully connected deep neural network regression estimates.The Annals of Statistics, 49(4):2231–2249, 2021

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.501442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6354adf3-3451-4412-9b50-6560a4191565 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.505487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63621c65-914b-43c9-82b6-f2b1cc4e7b94 · outbound

This paper cites Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.509970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1507092-53b8-4843-b7a9-e4a4ea78092c · outbound

This paper cites Nonasymptotic bounds for adversarial excess risk under misspecified models.SIAM Journal on Mathematics of Data Science, 6(4):847–868, 2024.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Nonasymptotic bounds for adversarial excess risk under misspecified models.SIAM Journal on Mathematics of Data Science, 6(4):847–868, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:50.015124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a9043595-5b26-431c-9992-c9cc454b9f10 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks.stat, 1050(9), 2017.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Towards deep learning models resistant to adversarial attacks.stat, 1050(9), 2017

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.518713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab83bdc3-640c-44df-904a-b3878ff433fa · outbound

This paper cites Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations.Mathematical Programming, 171(1):115–166, 2018.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations.Mathematical Programming, 171(1):115–166, 2018

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:49.988184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 08194289-39fb-472f-a888-7393dd1f70b3 · outbound

This paper cites A simple way to make neural networks robust against diverse image corruptions.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation A simple way to make neural networks robust against diverse image corruptions

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:49.972467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 55d7d5a6-b0e2-4855-899a-332fc0f1a80b · outbound

This paper cites Nonparametric regression using deep neural networks with relu activation function.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Nonparametric regression using deep neural networks with relu activation function

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.531855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6fd6cb0-ded7-4547-9c74-bc9ee129fa63 · outbound

This paper cites Distributionally robust logistic regression.Advances in neural information processing systems, 28, 2015.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Distributionally robust logistic regression.Advances in neural information processing systems, 28, 2015

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:06:49.946167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a6d40ee1-e43f-4869-882d-03fd8065c992 · outbound

This paper cites Certifying Some Distributional Robustness with Principled Adversarial Training.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.540461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c442dc18-f0ef-482f-95d0-0c022f15d024 · outbound

This paper cites Intriguing properties of neural networks.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Intriguing properties of neural networks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.545443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3952d2f8-6b95-4200-a825-a711af83dd45 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.550257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6df3d87a-032e-4610-aee4-375718601fd3 · outbound

This paper cites Cambridge university press, 2018.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Cambridge university press, 2018

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.554848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bfb432a-e942-4505-9aa7-57c87be0c1a5 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.559435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8464f8c5-7204-4988-983e-644627b8d9dc · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation mixup: Beyond Empirical Risk Minimization

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.564438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22547074-f937-481d-a673-a9e0581a1cad · outbound

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

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation How Does Mixup Help With Robustness and Generalization?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T19:06:49.569263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fb1507b-2fc9-4e5e-b5d1-88dbf6f3b9dd · outbound

This paper cites Improving the robustness of deep neural networks via stability training.

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation Improving the robustness of deep neural networks via stability training

Reference 40

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T19:06:49.743292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 942b9f5c-9521-4b3f-90e2-69cd97e4ec58 · inbound

Unification and Optimization of Robust Supervised Learning cites this paper.

Unification and Optimization of Robust Supervised Learning DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:13:30.295146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.