Paper Citation Record · LEDGER

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.23977.

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

pith.paper-citation-record.v1
2506.23977 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:32:58.981877Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

28 of 28 outbound references displayed

verified exact2
verified fuzzy21
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a53c1a4c-d05e-49c7-8226-d08497901d4f · outbound

This paper cites Efficient and accurate estimation of lipschitz constants for deep neural networks,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Efficient and accurate estimation of lipschitz constants for deep neural networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.545325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c92f81e2-9650-4045-90c2-b424eb520a02 · outbound

This paper cites Goodfellow, Y.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Goodfellow, Y

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.528350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c8ab21f8-714d-49e4-9f2c-879fc3d1f783 · outbound

This paper cites Sequence to sequence learning with neural networks,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Sequence to sequence learning with neural networks,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.853529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cfcb171-d5e8-4e9c-9d07-2f27d3dfbac8 · outbound

This paper cites Globally-robust neural net- works,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Globally-robust neural net- works,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.499314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f014b281-7b92-4970-ad5b-6191c1ed7bcd · outbound

This paper cites A Berkeley View of Systems Challenges for AI.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks A Berkeley View of Systems Challenges for AI

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.863644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0190c98-f40b-47fc-b88b-3e91e947bea8 · outbound

This paper cites Intriguing properties of neural networks.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Intriguing properties of neural networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.869407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b53ac602-03a3-4c32-8384-6a9a2454f3c0 · outbound

This paper cites Direct parameterization of lipschitz- bounded deep networks,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Direct parameterization of lipschitz- bounded deep networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.478716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1a30aeea-b6c4-4df1-95b2-54dd3e0fdd63 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.880365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd1f6c90-935f-42a5-af06-8d2c20417fc3 · outbound

This paper cites To- wards defending multiple lp-norm bounded adversarial perturbations via gated batch normalization,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks To- wards defending multiple lp-norm bounded adversarial perturbations via gated batch normalization,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.447472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47b207ff-1864-4a5f-a3ae-062e66b43fe7 · outbound

This paper cites Certified ro- bustness via dynamic margin maximization and improved lipschitz regularization,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Certified ro- bustness via dynamic margin maximization and improved lipschitz regularization,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.425690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6194965e-d364-4de3-ae84-e6b677adbcea · outbound

This paper cites Training robust neural networks using lipschitz bounds,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Training robust neural networks using lipschitz bounds,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.405398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9ca8f32d-a1b2-42cc-817d-9d69094f65d5 · outbound

This paper cites Safe learning in robotics: From learning-based control to safe reinforcement learning,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Safe learning in robotics: From learning-based control to safe reinforcement learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T21:32:58.899943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c712c154-0097-4549-b192-fbb3c699e66c · outbound

This paper cites Spectrally-normalized margin bounds for neural networks,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Spectrally-normalized margin bounds for neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.375108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 00bcb5b8-db10-4f4d-b979-7db529d4849e · outbound

This paper cites Lipschitz regularity of deep neural networks: analysis and efficient estimation,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Lipschitz regularity of deep neural networks: analysis and efficient estimation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.356897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ef56b123-94b7-4e38-9ac7-9303c8c7a962 · outbound

This paper cites Regularisation of neural networks by enforcing lipschitz continuity,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Regularisation of neural networks by enforcing lipschitz continuity,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.339950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 38c55ea9-2c0e-4ef1-ae66-f67415e7995c · outbound

This paper cites Verification of Non-Linear Specifications for Neural Networks.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Verification of Non-Linear Specifications for Neural Networks

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:32:59.060301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7b5198bb-76c6-413d-a7d1-2ae9158c7f46 · outbound

This paper cites Efficiently computing local lipschitz constants of neural networks via bound propagation,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Efficiently computing local lipschitz constants of neural networks via bound propagation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.322404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b601a708-b535-4870-97d6-cf08e08a9127 · outbound

This paper cites Chordal sparsity for sdp-based neural network verification,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Chordal sparsity for sdp-based neural network verification,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.306313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 013dec9d-0ee1-4666-88e4-0ae00b8776a8 · outbound

This paper cites Imitation learning with stability and safety guarantees,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Imitation learning with stability and safety guarantees,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.282503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 437cfbde-9307-4778-973a-289b0d1a55c7 · outbound

This paper cites Learning neural networks under input-output specifications,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Learning neural networks under input-output specifications,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.265372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e7b7a4c7-72b9-4900-ac3e-e10ea875316c · outbound

This paper cites Neural network training under semidefinite constraints,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Neural network training under semidefinite constraints,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.249937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 066f82a0-912d-4dbe-99f6-9abf64009aa2 · outbound

This paper cites Chordal and factor-width decompositions for scalable semidefinite and polynomial optimization,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Chordal and factor-width decompositions for scalable semidefinite and polynomial optimization,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.232460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 552fd2f6-c9aa-4cf5-badb-c6e2173ca7a9 · outbound

This paper cites Cosmo: A conic operator splitting method for convex conic problems,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Cosmo: A conic operator splitting method for convex conic problems,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.202281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation faf571c5-8dcf-4e3c-a173-eef8d204b974 · outbound

This paper cites On the scalability and memory efficiency of semidefinite programs for lipschitz constant estimation of neural networks,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks On the scalability and memory efficiency of semidefinite programs for lipschitz constant estimation of neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.180597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation de42dd65-0663-4869-91e1-e59e5e24e0a3 · outbound

This paper cites On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:32:59.033488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8f885437-4b77-4064-9d48-dd5f0c01a476 · outbound

This paper cites Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.161299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 64464053-16de-4649-9cb4-fe47ab7ab47f · outbound

This paper cites S-procedure in nolinear control theory,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks S-procedure in nolinear control theory,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.141496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb4ab0b5-54fc-4eca-bf74-a0a72ab881a3 · outbound

This paper cites Randomized sketches of convex programs with sharp guarantees,.

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks Randomized sketches of convex programs with sharp guarantees,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:32:59.122439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.