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Paper Citation Record · LEDGER

Smooth Learning with Hard Constraints via Legendre-Regularized Policies

As of 18 August 2026, this Paper Citation Record lists 92 of 92 outbound references and 0 inbound Pith citation observations for arXiv:2607.24007.

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

pith.paper-citation-record.v1
2607.24007 v1

Coverage vector

measured 92 of 92 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:30:25.048319Z

measured 92 of 92 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

92 of 92 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved91
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4ea29477-ef99-494c-bb12-4017a7896cd4 · outbound

This paper cites predict, then optimize.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies predict, then optimize

Reference 1

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source=arxiv_source observed=2026-07-31T23:30:16.093389Z digest=sha256:c41dc864b5c32538b8d83be54d839a5ca68f765724b858ab7b3f6906c06c6174

Observation 38d2be33-13e9-4046-ad42-76c3e0f36dcc · outbound

This paper cites Management Science , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Management Science , volume=

Reference 2

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source=arxiv_source observed=2026-07-31T23:30:16.157227Z digest=sha256:fced340f25a6729aa56fbda121baee8e405cb3738cbffed08e5f930f0a2171c6

Observation 21d43b77-06b0-4bd5-9dda-318064fb86ff · outbound

This paper cites 2013 , publisher=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies 2013 , publisher=

Reference 3

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source=arxiv_source observed=2026-07-31T23:30:16.212001Z digest=sha256:13802dd5fa9110ef16271f2e16b9ba65b7f72c9bf09b52b18a965e802315664c

Observation 36199906-e343-4955-9908-49d03096465b · outbound

This paper cites Neural networks , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Neural networks , volume=

Reference 4

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source=arxiv_source observed=2026-07-31T23:30:16.252364Z digest=sha256:2f5ecdb6436cf8b310a4fb6a951513bbff4f341e91b5b4c8d434414560b586c4

Observation d671b7f1-10d8-45df-a52b-9d21ba40a8f6 · outbound

This paper cites Neural networks , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Neural networks , volume=

Reference 5

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Observation 20777408-08e6-4fdf-8339-4ee01c0e25d9 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 6

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Observation 23de318d-d5da-47e8-aac0-30551f1f6580 · outbound

This paper cites Mathematical Programming Computation , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Mathematical Programming Computation , volume=

Reference 7

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Observation 3718fb7a-02be-4fbb-a0b3-016116d13a0d · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Advances in Neural Information Processing Systems , volume=

Reference 8

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Observation c655fd8e-2683-4a1a-bb51-61d578703277 · outbound

This paper cites an unresolved cited work.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Unresolved cited work

Reference 9

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source=arxiv_source observed=2026-07-31T23:30:16.540193Z digest=sha256:d67d28aee6ac2ce5069bb35b38a9d74c5ee5735797e51781420279ff574d8538

Observation d48f4d9e-fb0f-42e0-98e9-06412d39fa1e · outbound

This paper cites Mathematical programming , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Mathematical programming , volume=

Reference 10

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Observation bd0186d2-afd0-4a00-880b-cccbcbb4c708 · outbound

This paper cites Conference on Learning Theory , pages=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Conference on Learning Theory , pages=

Reference 11

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source=arxiv_source observed=2026-07-31T23:30:16.625424Z digest=sha256:5eda5c77108ac34d807ea83e9b5fac06f49577c8dc6aaa908ff5414d18c694b6

Observation 925c58f4-ac1c-4b0e-be20-37e2f313fd7e · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Advances in Neural Information Processing Systems , volume=

Reference 12

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Observation 5db7c17e-44ed-41bc-b1e4-eb0a9354d898 · outbound

This paper cites 1970 , publisher=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies 1970 , publisher=

Reference 13

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Observation 111e98f5-927b-4d37-bfbf-72c01fc0729e · outbound

This paper cites European Journal of Operational Research , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies European Journal of Operational Research , volume=

Reference 14

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Observation de999c3e-cf4e-431e-b2e1-f57ba166fadd · outbound

This paper cites Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=

Reference 15

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Observation 0bd4fa11-b041-4dd7-a816-5d13e77fb8ed · outbound

This paper cites A Universal End-to-End Approach to Portfolio Optimization via Deep Learning.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies A Universal End-to-End Approach to Portfolio Optimization via Deep Learning

Reference 16

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Observation 48333572-7f68-43d2-b9ae-3eb8655bcd30 · outbound

This paper cites IEEE Transactions on Power Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies IEEE Transactions on Power Systems , volume=

Reference 17

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Observation 4afdb2f8-97eb-4365-8936-6fa5a822579a · outbound

This paper cites Operations Research , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Operations Research , volume=

Reference 18

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source=arxiv_source observed=2026-07-31T23:30:17.023471Z digest=sha256:6f1b0b87470f7a49e051d1315a6821223a03892a3b637de7ff4eef5a9b814d5d

Observation af039af7-4f15-45d4-80cf-c158d8e71de1 · outbound

This paper cites Management Science , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Management Science , volume=

Reference 19

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Observation 989b2ec2-1613-4c51-a3e0-bc34bfdb0c65 · outbound

This paper cites On Data-Driven Prescriptive Analytics with Side Information: A Regularized.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies On Data-Driven Prescriptive Analytics with Side Information: A Regularized

Reference 20

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Observation bf147adc-e8d8-45bf-9d73-49399cf384f8 · outbound

This paper cites Operations Research , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Operations Research , volume=

Reference 21

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Observation 14dae24b-26f3-42c8-ad04-27c325de80aa · outbound

This paper cites Mathematical Programming , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Mathematical Programming , volume=

Reference 22

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Observation 20bc8e37-cec6-4eff-84a2-adf5a260d7ff · outbound

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Smooth Learning with Hard Constraints via Legendre-Regularized Policies 2022 , eprint=

Reference 23

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Observation f4e806da-5e50-4cd3-ab3f-ac95d1c50d2d · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Advances in Neural Information Processing Systems , volume=

Reference 24

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Observation fb98c202-f1a5-4795-b627-899781fbe12e · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Advances in Neural Information Processing Systems , volume=

Reference 25

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Observation f4301313-a5df-4d36-89e2-f7e27885504e · outbound

This paper cites International Conference on Machine Learning , pages=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies International Conference on Machine Learning , pages=

Reference 26

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Observation 1162bdc8-994f-4756-8fa5-f3e8e322b2ce · outbound

This paper cites Operations Research , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Operations Research , volume=

Reference 27

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Observation 84dc5aeb-d62f-424a-bf8e-41de546a74d4 · outbound

This paper cites Operations Research , year=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Operations Research , year=

Reference 28

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source=arxiv_source observed=2026-07-31T23:30:18.009778Z digest=sha256:bc9633aaaa6592034ae5256678147491c56862b9ef834afa4024435eb0ff07f4

Observation 5109a45b-a5c8-46b6-bfaa-402eea71fb38 · outbound

This paper cites Management Science , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Management Science , volume=

Reference 29

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Observation 01b0f512-899d-4d2f-befd-b58abcb20756 · outbound

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Smooth Learning with Hard Constraints via Legendre-Regularized Policies Iise Transactions , volume=

Reference 31

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Observation 60954564-fdb4-4d0d-b458-5d0b6f92f8c8 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Advances in Neural Information Processing Systems , volume=

Reference 32

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Observation c40903c1-b7a5-49e3-9b75-f4d38c33210f · outbound

This paper cites European Journal of Operational Research , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies European Journal of Operational Research , volume=

Reference 33

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Observation af1e5b26-e28f-4afc-b1c2-3e73970996db · outbound

This paper cites The Review of Financial Studies , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies The Review of Financial Studies , volume=

Reference 34

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Observation dd973528-4855-43a9-8ce6-b1bb033fb7b0 · outbound

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Smooth Learning with Hard Constraints via Legendre-Regularized Policies INFOR: Information Systems and Operational Research , volume=

Reference 35

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Observation 188c1032-ce89-4686-9bdf-cc347c03d80e · outbound

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Smooth Learning with Hard Constraints via Legendre-Regularized Policies arXiv preprint arXiv:2509.14557 , year=

Reference 36

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Observation 7a11ddb4-1255-4cb1-a448-9878bb77a275 · outbound

This paper cites Operations Research , year=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Operations Research , year=

Reference 37

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Observation d8a2c79c-d889-457e-82a2-63d979c5f8d1 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Advances in Neural Information Processing Systems , volume=

Reference 38

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Observation e5495216-3345-42ec-a327-45569e33b376 · outbound

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Smooth Learning with Hard Constraints via Legendre-Regularized Policies Available at SSRN 3623006 , year=

Reference 39

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Observation 2792a815-cdc9-427f-8ac0-b8aeab85d154 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 40

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source=arxiv_source observed=2026-07-31T23:30:19.169337Z digest=sha256:2805cef24900f839729d69cfe7c708ee129698b0ea396a29058d249fc4612084

Observation a1625fe3-45ae-4baa-8440-19b4f6ca442f · outbound

This paper cites International Conference on Machine Learning , pages=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies International Conference on Machine Learning , pages=

Reference 41

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Observation 9a899bb9-29d8-455b-96fb-01a6d6ce406c · outbound

This paper cites Management Science , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Management Science , volume=

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Observation c8a10e37-3534-4e9d-944a-4e82f097b6cc · outbound

This paper cites Unpublished Manuscript, http://ttic.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Unpublished Manuscript, http://ttic

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Observation 3cbfdf23-52df-4799-9d55-597006dfb0ee · outbound

This paper cites 2009 , publisher=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies 2009 , publisher=

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Observation 3b62e66c-b885-41eb-bf1a-0a2f24a7f642 · outbound

This paper cites Practical.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Practical

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Observation 2f48b5f8-6a2e-4f5f-bd28-479ea111152b · outbound

This paper cites Journal of Global optimization , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Journal of Global optimization , volume=

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Observation 97906c18-3801-4300-b9d7-ae6f33d38054 · outbound

This paper cites International Conference on Artificial Intelligence and Statistics , pages=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies International Conference on Artificial Intelligence and Statistics , pages=

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Observation b895fd24-0f01-4c50-8307-636b43618a1e · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Advances in Neural Information Processing Systems , volume=

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Observation 5e43eb30-4ed7-4ff4-83b2-fa670cc8a945 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Advances in Neural Information Processing Systems , volume=

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Observation 2e851c92-0173-4ac2-95a0-7ab64ff24241 · outbound

This paper cites , title =.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies , title =

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Observation f3b6e5f6-6c02-431c-a6fe-8fd339bb64a7 · outbound

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Smooth Learning with Hard Constraints via Legendre-Regularized Policies , author=

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Observation b8a9dab2-4865-4690-951c-d9b0c99e47aa · outbound

This paper cites Learning with.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Learning with

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Observation 2f93dbea-a5a1-4af7-bd1d-e33570f3c704 · outbound

This paper cites Machine learning meets.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Machine learning meets

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Observation 98d8ec9b-9a8c-4810-a464-6445c53fc2c1 · outbound

This paper cites Online linear optimization via smoothing.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Online linear optimization via smoothing

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Observation f3ea18ee-e50c-417c-b2c9-0c838f9fcec9 · outbound

This paper cites Differentiable convex optimization layers.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Differentiable convex optimization layers

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Observation f413e99a-b380-497a-a3fc-ac236a061125 · outbound

This paper cites Optnet: Differentiable optimization as a layer in neural networks.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Optnet: Differentiable optimization as a layer in neural networks

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Observation 5fe6e686-f7e4-4a59-bc96-e334f019f644 · outbound

This paper cites The big data newsvendor: Practical insights from machine learning.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies The big data newsvendor: Practical insights from machine learning

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Observation dc3e8767-8a77-471b-a488-c8ca8a6e6666 · outbound

This paper cites Generalization bounds for regularized portfolio selection with market side information.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Generalization bounds for regularized portfolio selection with market side information

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Observation 8c733647-88ea-4a04-808e-5bfe9fd1d6fe · outbound

This paper cites Learning with differentiable perturbed optimizers.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Learning with differentiable perturbed optimizers

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Observation d0ad8589-1ed9-4ea4-9f00-23c3b11cefb1 · outbound

This paper cites Control of uncertain systems with a set-membership description of the uncertainty.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Control of uncertain systems with a set-membership description of the uncertainty

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Observation e5cd3826-b640-4d30-8b08-48351dcd501c · outbound

This paper cites From predictive to prescriptive analytics.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies From predictive to prescriptive analytics

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Observation 9c05d3a3-7c06-4b6e-80a9-0d98ece95fd4 · outbound

This paper cites Data-driven optimization: A reproducing kernel hilbert space approach.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Data-driven optimization: A reproducing kernel hilbert space approach

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Observation 43956e2c-08ce-4287-9832-a9d8d6f6582e · outbound

This paper cites Dynamic optimization with side information.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Dynamic optimization with side information

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Observation ce130e5f-abed-4820-a2e7-71129a506d48 · outbound

This paper cites Learning with F enchel- Y oung losses.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Learning with F enchel- Y oung losses

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Observation 95a7c030-d533-47e5-aaaa-efbd8fe4631a · outbound

This paper cites Nonsmooth implicit differentiation for machine-learning and optimization.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Nonsmooth implicit differentiation for machine-learning and optimization

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Observation a6bc360c-e729-4ef3-b990-39b0a9970601 · outbound

This paper cites Parametric portfolio policies: Exploiting characteristics in the cross-section of equity returns.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Parametric portfolio policies: Exploiting characteristics in the cross-section of equity returns

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Observation 101f3e32-5c92-4aa7-98bd-bae8475cbc8e · outbound

This paper cites End-to-end feasible optimization proxies for large-scale economic dispatch.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies End-to-end feasible optimization proxies for large-scale economic dispatch

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source=arxiv_source observed=2026-07-31T23:30:22.691936Z digest=sha256:5995fe962c1729d8936bb37ef96ede57ac86ec4fd10a0bdd879ee6ed2efdaa3e

Observation 163abd58-401e-4b64-8936-fc4666d2634f · outbound

This paper cites Task-based end-to-end model learning in stochastic optimization.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Task-based end-to-end model learning in stochastic optimization

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source=arxiv_source observed=2026-07-31T23:30:22.874202Z digest=sha256:af9c11a5a46f277d468814e8616004c1feee37cd84a7e0bc7f922e8e32af9b4d

Observation d636bce0-c13a-4b0f-82ee-7c7893810967 · outbound

This paper cites predict, then optimize.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies predict, then optimize

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Observation 2500e1c8-5643-4ebc-a598-8be5f1aec0f3 · outbound

This paper cites Decision trees for decision-making under the predict-then-optimize framework.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Decision trees for decision-making under the predict-then-optimize framework

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Observation 40f48950-ba4e-4c74-9f60-4f77a6886533 · outbound

This paper cites Mind the (optimality) gap: a gap-aware learning rate scheduler for adversarial nets.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Mind the (optimality) gap: a gap-aware learning rate scheduler for adversarial nets

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source=arxiv_source observed=2026-07-31T23:30:23.332397Z digest=sha256:b2432382d5a07b0e6f1b907478a6714c363c86c2e289110e913fff184082a40b

Observation f0f31f71-4bf1-4383-8758-947ac31f57fa · outbound

This paper cites Efficient global optimization of expensive black-box functions.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Efficient global optimization of expensive black-box functions

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source=arxiv_source observed=2026-07-31T23:30:23.464070Z digest=sha256:b83fcc9ba3d5374cf74dd5881019e20d03c959fdfdbd1a48dabd1e7e2e136af9

Observation dc7db5af-0a45-41f7-8bb2-abd835f3f5a0 · outbound

This paper cites Stochastic optimization forests.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Stochastic optimization forests

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Observation a6f6953c-0a7a-4e9b-9cd8-78291d7cfb0b · outbound

This paper cites an unresolved cited work.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Unresolved cited work

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Observation 3122cd8f-05cf-4a80-b81e-ab80392f89ca · outbound

This paper cites an unresolved cited work.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Unresolved cited work

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Observation 7db0f9b6-68fd-4354-b8c4-747306fe9655 · outbound

This paper cites Data-driven sample average approximation with covariate information.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Data-driven sample average approximation with covariate information

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source=arxiv_source observed=2026-07-31T23:30:23.806883Z digest=sha256:b8feb95eb1db952961fd11783288159dfa6f13d59c5e970e06441f2c405790b8

Observation 66505624-61fa-46c7-917a-aeeda4b20d67 · outbound

This paper cites Risk bounds and calibration for a smart predict-then-optimize method.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Risk bounds and calibration for a smart predict-then-optimize method

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Observation 14478240-3144-42f4-8e4e-5233beee5b92 · outbound

This paper cites Decision-driven regularization: A blended model for predict-then-optimize.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Decision-driven regularization: A blended model for predict-then-optimize

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source=arxiv_source observed=2026-07-31T23:30:23.949147Z digest=sha256:a4bbc1e37e09c45998bd59eb99e9e31f84e7d6fa3836fb4cd3d38bf0815f3171

Observation 0a6f248d-e809-430f-839f-ce0c8c06ba19 · outbound

This paper cites Smart predict-and-optimize for hard combinatorial optimization problems.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Smart predict-and-optimize for hard combinatorial optimization problems

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source=arxiv_source observed=2026-07-31T23:30:24.026970Z digest=sha256:070bb2683a46ee6fb6c2a473e38a9862fa68ded018eb0e0f4b48ed85b23c9d3d

Observation cb43cdfa-804e-4853-85a5-8e2445bea76d · outbound

This paper cites Calibration by distribution matching: Trainable kernel calibration metrics.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Calibration by distribution matching: Trainable kernel calibration metrics

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source=arxiv_source observed=2026-07-31T23:30:24.105321Z digest=sha256:ecdac20cb107ea33916f3805febef0d97f53d3552f1ba8c814c8e8c5e8d90fbd

Observation 31f1a0bb-7283-4ce4-ad7d-77a85d07658e · outbound

This paper cites Smooth minimization of non-smooth functions.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Smooth minimization of non-smooth functions

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source=arxiv_source observed=2026-07-31T23:30:24.181111Z digest=sha256:98598f2713b0ab7ed02f900cf06afadba6d1781482824236ce7fce946ae28f04

Observation c0596e75-79aa-47e2-9d6f-89a8a3fe85a6 · outbound

This paper cites Sparse MAP : Differentiable sparse structured inference.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Sparse MAP : Differentiable sparse structured inference

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source=arxiv_source observed=2026-07-31T23:30:24.250315Z digest=sha256:bfc3102ed5bbb4a7e8d0455d719ed6681cd3f48c782c4f2053d5db53d6d097f3

Observation a3b3ec16-8b8f-4c0e-a56a-8c5b05cbdb6b · outbound

This paper cites Applying deep learning to the newsvendor problem.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Applying deep learning to the newsvendor problem

Reference 84

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source=arxiv_source observed=2026-07-31T23:30:24.297504Z digest=sha256:454c09f13675f8c3ea81ad325090f27db72e8dfd0ff450e9c6d794fe13d88f7f

Observation 7c778268-319b-4b6a-aa05-4f287dca9813 · outbound

This paper cites A practical end-to-end inventory management model with deep learning.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies A practical end-to-end inventory management model with deep learning

Reference 85

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source=arxiv_source observed=2026-07-31T23:30:24.375612Z digest=sha256:fe99e203b20daa738c8adbe3d6e6fec09204a29c67ba628de3e6cf2a23a98ccb

Observation 855735d2-63de-4080-bfb5-5b6ea7e1b649 · outbound

This paper cites Tyrrell Rockafellar.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Tyrrell Rockafellar

Reference 86

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source=arxiv_source observed=2026-07-31T23:30:24.455699Z digest=sha256:55d4d1bfc05035b1c05957801e550a3e642f2f1c6b5c74999f1cc903ae9aabe0

Observation bb0bc36d-0830-453c-a3e4-acf6074cc959 · outbound

This paper cites Tyrrell Rockafellar and Roger J.-B.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Tyrrell Rockafellar and Roger J.-B

Reference 87

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source=arxiv_source observed=2026-07-31T23:30:24.538815Z digest=sha256:dbe77f0d7bc0de0178a8daa9e2685895cb5877bea00be678a894b2c6687604bd

Observation fc489cd9-4f8f-46c8-9fad-40bb1cedf27f · outbound

This paper cites Schneider and Daniel Kuhn.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Schneider and Daniel Kuhn

Reference 88

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source=arxiv_source observed=2026-07-31T23:30:24.623984Z digest=sha256:62d0d39a4303495b4c3f5d65b7d2509ae146028b479cf3cd5bc4504d11b3bdfc

Observation 3da58654-7af3-4948-a63e-8b1e711593e2 · outbound

This paper cites Practical B ayesian optimization of machine learning algorithms.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Practical B ayesian optimization of machine learning algorithms

Reference 89

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source=arxiv_source observed=2026-07-31T23:30:24.693806Z digest=sha256:195d006d35be3dfe71c4e13efee8b004076c6509d6029d02aa99e22fdf484f7c

Observation b6483ff3-4a14-4468-ab0b-a37807524caa · outbound

This paper cites Machine learning meets M arkowitz.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Machine learning meets M arkowitz

Reference 90

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source=arxiv_source observed=2026-07-31T23:30:24.746277Z digest=sha256:dc0f75f3c1abc505e3648b993402626e546cd38af328dd7eb5e0adb5e6d3c390

Observation 8cc360ff-239a-482c-8aba-6b19d95fff0c · outbound

This paper cites On data-driven prescriptive analytics with side information: A regularized N adaraya-- W atson approach.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies On data-driven prescriptive analytics with side information: A regularized N adaraya-- W atson approach

Reference 91

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source=arxiv_source observed=2026-07-31T23:30:24.827967Z digest=sha256:74404bb46abbcc32b42e24a92482570b2f711b58a3f4380009a0a4a9ffda7710

Observation 73339bb1-28a7-4f28-84dd-93ce1fcdaf63 · outbound

This paper cites Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Melding the data-decisions pipeline: Decision-focused learning for combinatorial optimization

Reference 92

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source=arxiv_source observed=2026-07-31T23:30:24.905147Z digest=sha256:8e7bf65bb9cdaa817a56a02c45b5ea00ca789b4562d82aaf68c2a5e2e61ceb7f

Observation 2aef28d5-eed9-4f38-81ca-dfd8c92796c8 · outbound

This paper cites Data-driven piecewise affine decision rules for stochastic programming with covariate information.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Data-driven piecewise affine decision rules for stochastic programming with covariate information

Reference 93

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source=arxiv_source observed=2026-07-31T23:30:24.981357Z digest=sha256:28036cba8f071d8df2f74570264763bb40a49d074cad91dce828c9ed12539971

Observation 69b83209-aa86-4c63-97fc-db3b74b0060c · outbound

This paper cites Deep Learning for Portfolio Optimization.

Smooth Learning with Hard Constraints via Legendre-Regularized Policies Deep Learning for Portfolio Optimization

Reference 94

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source=arxiv_source observed=2026-07-31T23:30:25.048319Z digest=sha256:f447418e8038250369a3db04128988e4a236d3331aeb9023eabb48c287e4795e

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