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Paper Citation Record · LEDGER

Certifying Some Distributional Robustness with Principled Adversarial Training

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:1710.10571.

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

pith.paper-citation-record.v1
1710.10571 v5

Coverage vector

measured 0 of 0 reference resolution

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Source: paper_references, paper_reference_links

measured 31 of 31 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:14:38.826853Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

346
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2dd2e29e-31f7-4bdf-90b2-5979171ffdb5 · inbound

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems cites this paper.

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 49

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arxiv_id, observed 2026-05-11T11:33:21.220075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 57d40b0e-3ef7-499f-ae08-f6120b8508bd · inbound

Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems cites this paper.

Robust SGLD algorithm for solving non-convex distributionally robust optimisation problems Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 122

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arxiv_id, observed 2026-05-24T02:43:47.628269Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 1f5af60f-0547-42fa-9fe7-0ca404aa3417 · inbound

Toward Robust Neural Reconstruction from Sparse Point Sets cites this paper.

Toward Robust Neural Reconstruction from Sparse Point Sets Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 79

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no resolver link, observed 2026-08-11T10:42:49.299692Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:42:49.299692Z digest=sha256:aba033ee49553785137cbab37c3f558c13abba084722bde3160ae070738bf52e

Observation 5cbfd8a8-6548-4c24-8123-5cbb01d1afa2 · inbound

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces cites this paper.

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 27

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no resolver link, observed 2026-08-10T23:26:24.789734Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T23:26:24.789734Z digest=sha256:643e87a9747498e2eb40f964fd54aa482ce382bc21df72eb95cd70077276bde7

Observation e1e1a63e-05cb-461e-8705-83e75ce0d890 · inbound

Decentralized Min-Max Optimization with Gradient Tracking cites this paper.

Decentralized Min-Max Optimization with Gradient Tracking Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 31

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no resolver link, observed 2026-08-15T21:14:38.826853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:14:38.826853Z digest=sha256:a51e84ec5eec2df440a07c2e4b9d6344defa74602f09a46c0bf3cb8c6a82c7d9

Observation bad7c10f-9333-457f-928a-75592f9ee03f · inbound

When Shift Happens - Confounding Is to Blame cites this paper.

When Shift Happens - Confounding Is to Blame Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 75

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no resolver link, observed 2026-08-07T13:40:21.948421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:40:21.948421Z digest=sha256:4552ba27f5c8ed55bb25c3edde9b0042955d42160228f192fa8e753e74009020

Observation 872eb3c9-cdd6-46f7-824d-762c067ef757 · inbound

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models cites this paper.

FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 62

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no resolver link, observed 2026-08-06T23:51:00.164072Z

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source=arxiv_source observed=2026-08-06T23:51:00.164072Z digest=sha256:aba2776398b3c88e1eed07c5d09df338eb4e04bdcf28f370c79994a8dc48b8a6

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

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation cites this paper.

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

Reference 33

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

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Unavailable: canonical work link unavailable.

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Observation 2c7be32c-07bd-402c-9654-5997a4148884 · inbound

A New Perspective On AI Safety Through Control Theory Methodologies cites this paper.

A New Perspective On AI Safety Through Control Theory Methodologies Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 91

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:38:26.029403Z digest=sha256:aebf3db482d9e6dfc777aff5e8f36304566f2e98ce94e1eb92568d10fee12409

Observation b9336108-d6a0-4497-964b-c67a176e6daf · inbound

A first-order method for nonconvex-nonconcave minimax problems under a local Kurdyka-Lojasiewicz condition cites this paper.

A first-order method for nonconvex-nonconcave minimax problems under a local Kurdyka-Lojasiewicz condition Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 26

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verified exact
arxiv_id, observed 2026-05-21T23:35:45.714678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation bc9e8de9-2419-4b17-b5d6-72137e5810ed · inbound

Understanding Knowledge Transferability for Transfer Learning: A Survey cites this paper.

Understanding Knowledge Transferability for Transfer Learning: A Survey Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 76

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Observation dfdb64a4-401b-45da-b8db-89bf88fe795b · inbound

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization cites this paper.

An Optimistic Gradient Tracking Method for Distributed Minimax Optimization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 2

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no resolver link, observed 2026-08-05T14:28:34.612349Z

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Observation da2d90b1-b867-4696-adb8-e1a8bf70a2d2 · inbound

Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty cites this paper.

Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 19

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Observation 66a2b13d-0413-4cd4-ad3c-169aa5fbb748 · inbound

Robustifying Diffusion-Denoised Smoothing Against Covariate Shift cites this paper.

Robustifying Diffusion-Denoised Smoothing Against Covariate Shift Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 25

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T17:30:02.248572Z digest=sha256:6312c9c82d29557e71845f818d9aa06573903be978c7a6ba6d2d88fa38a4bdbc

Observation 48bf6cd9-bfd6-4f8b-b027-1c58c23ef108 · inbound

Improved Stochastic Optimization of LogSumExp cites this paper.

Improved Stochastic Optimization of LogSumExp Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 30

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no resolver link, observed 2026-08-04T13:54:24.304742Z

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source=arxiv_source observed=2026-08-04T13:54:24.304742Z digest=sha256:318d65c0154221e2521a598ffe32b05316579cac6911c51d08cc5776c4863f1a

Observation 1fc03eca-7269-429b-920c-8873d4713db3 · inbound

A first-order method for constrained nonconvex-nonconcave minimax optimization cites this paper.

A first-order method for constrained nonconvex-nonconcave minimax optimization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 26

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Observation 238f0d35-122d-4d9d-8393-7d993427be90 · inbound

Gradient Flow Sampler-based Distributionally Robust Optimization cites this paper.

Gradient Flow Sampler-based Distributionally Robust Optimization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 32

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Observation 5f7da2bc-5e21-494b-a307-44d7d38664c6 · inbound

Multivariate Time Series Data Imputation via Distributionally Robust Regularization cites this paper.

Multivariate Time Series Data Imputation via Distributionally Robust Regularization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 55

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verified exact
arxiv_id, observed 2026-05-16T08:42:36.972496Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 46cc1d35-cf3f-4d3a-a030-5919ea97bc5e · inbound

Bigger Is Safer: Provable Robustness in In-Context Learning Scales with Capacity cites this paper.

Bigger Is Safer: Provable Robustness in In-Context Learning Scales with Capacity Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 11

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Observation 7f09cdad-8c9b-42e2-9bb6-4d4def9fc6dc · inbound

Generative models for decision-making under distributional shift cites this paper.

Generative models for decision-making under distributional shift Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 13

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arxiv_id, observed 2026-05-10T21:55:51.941632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation a4fe4a40-a333-43b6-95cd-4f95f6936e6f · inbound

Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization cites this paper.

Adversarial Label Invariant Graph Data Augmentations for Out-of-Distribution Generalization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 5

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arxiv_id, observed 2026-05-11T07:41:01.655550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ecbd4be2-08e8-44dd-8cb7-6de2d2195728 · inbound

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers cites this paper.

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 115

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arxiv_id, observed 2026-05-10T15:40:33.097233Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 7cfe2c26-d859-48f0-b7bc-58005cff7edf · inbound

A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch cites this paper.

A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 23

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arxiv_id, observed 2026-05-09T01:59:36.018469Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ea387019-ba3d-4af7-9940-32e29672bc61 · inbound

Robust Representation Learning through Explicit Environment Modeling cites this paper.

Robust Representation Learning through Explicit Environment Modeling Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 31

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arxiv_id, observed 2026-05-12T00:46:12.925875Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-07T14:21:27.965959Z digest=sha256:876f4a0ed8c109652ac8c8c1fdd8dbf62afdd59af1ab7cfa9e4351b039389f74

Observation 94392920-11f3-44db-a3fa-76c2d38cab80 · inbound

Distributionally Robust Multi-Objective Optimization cites this paper.

Distributionally Robust Multi-Objective Optimization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 40

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arxiv_id, observed 2026-05-11T18:36:08.406743Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-08T15:00:37.589354Z digest=sha256:dc9d9ba20b8a85e71793b71558c968d40ea5e1dc6ad60da87cb5533081fd3181

Observation 813e46d4-2c4a-4386-b132-eea4f69e4d4b · inbound

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data cites this paper.

Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 42

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arxiv_id, observed 2026-05-13T05:57:21.620183Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-13T05:56:38.042978Z digest=sha256:305637aab37e437cba2a491e4cc2be25140a3108ca461380d00b47ae3abfdd3a

Observation ef9cc49c-c950-4b3b-b723-9ffa61335002 · inbound

Lipschitz Optimization for Formal Verification of Homographies cites this paper.

Lipschitz Optimization for Formal Verification of Homographies Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 44

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arxiv_id, observed 2026-05-25T05:00:22.240908Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation af43a220-cfc3-431f-973b-79557b9af0f7 · inbound

A Single-Loop Regularized Newton Method for Nonconvex-Strongly-Concave Minimax Optimization cites this paper.

A Single-Loop Regularized Newton Method for Nonconvex-Strongly-Concave Minimax Optimization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 33

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arxiv_id, observed 2026-07-02T19:17:18.495905Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-27T21:32:49.075190Z digest=sha256:854567dd80ad047becfedd4ffaedf689580864291cfb9f9425b33537c9cf8a56

Observation e05ba2fb-9953-4066-bfc6-96a436a34447 · inbound

Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization cites this paper.

Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 62

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arxiv_id, observed 2026-07-03T09:57:56.243589Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-27T10:17:01.777614Z digest=sha256:ab9430b095c9d856cd7f01c69aed99eacb5a5573fee9b42989ab7d4130528464

Observation 8837ec2b-5c38-42f5-ba1f-f1d5644ad90d · inbound

Amnesia: A Stealthy Replay Attack on Continual Learning Dreams cites this paper.

Amnesia: A Stealthy Replay Attack on Continual Learning Dreams Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 28

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arxiv_id, observed 2026-07-03T12:18:06.665574Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-06-27T09:02:44.548488Z digest=sha256:64a910310e64b6e7c2bdf698acb1a0dce7e39e92784e36334108df01d5951847

Observation 3f57ffe0-5991-48b9-b0a3-e48c1125bdc6 · inbound

Distributionally Robust and Safe Imitation Learning cites this paper.

Distributionally Robust and Safe Imitation Learning Certifying Some Distributional Robustness with Principled Adversarial Training

Reference 19

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