Paper Citation Record · LEDGER

Towards Robust Learning to Optimize with Theoretical Guarantees

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2506.14263.

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

pith.paper-citation-record.v1
2506.14263 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:25:39.427571Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

43 of 43 outbound references displayed

verified exact1
verified fuzzy34
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 37edb52a-6a2f-42f8-bf5d-44a148db661a · outbound

This paper cites Ada-lista: Learned solvers adaptive to varying models.

Towards Robust Learning to Optimize with Theoretical Guarantees Ada-lista: Learned solvers adaptive to varying models

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:47.005520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b1ada911-54b0-4002-9353-178cefe2b08b · outbound

This paper cites A Generalizable Approach to Learning Optimizers.

Towards Robust Learning to Optimize with Theoretical Guarantees A Generalizable Approach to Learning Optimizers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T00:25:34.723979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a703a6c-d102-4f50-8489-467cd4d98107 · outbound

This paper cites Learning to learn by gradient descent by gradient descent.

Towards Robust Learning to Optimize with Theoretical Guarantees Learning to learn by gradient descent by gradient descent

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:46.827104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e25adb11-2abf-4604-b14d-499bd8364fd2 · outbound

This paper cites Online Learning Rate Adaptation with Hypergradient Descent.

Towards Robust Learning to Optimize with Theoretical Guarantees Online Learning Rate Adaptation with Hypergradient Descent

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T00:25:34.857654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e616fe86-d5a6-45de-b0e8-a6444ec12688 · outbound

This paper cites A fast iterative shrinkage- thresholding algorithm for linear inverse problems.

Towards Robust Learning to Optimize with Theoretical Guarantees A fast iterative shrinkage- thresholding algorithm for linear inverse problems

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:46.657710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae36220a-6c61-46f9-8bfa-37a0210b2a21 · outbound

This paper cites A Deep Q-Network Based-Resource Allocation Scheme for Massive MIMO-NOMA.

Towards Robust Learning to Optimize with Theoretical Guarantees A Deep Q-Network Based-Resource Allocation Scheme for Massive MIMO-NOMA

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:46.513343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 77e792fb-688d-47ef-8c8a-257a67d5f7b6 · outbound

This paper cites Learning to optimize: A primer and a benchmark.

Towards Robust Learning to Optimize with Theoretical Guarantees Learning to optimize: A primer and a benchmark

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:46.358189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 048169ff-b510-459c-8bd6-1a35dbd7e8dd · outbound

This paper cites Learning Fast Approxima- tions of Sparse Coding.

Towards Robust Learning to Optimize with Theoretical Guarantees Learning Fast Approxima- tions of Sparse Coding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:46.192594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation aa668d11-ccfc-4e68-bc81-385fb2093854 · outbound

This paper cites Safeguarded learned convex optimization.

Towards Robust Learning to Optimize with Theoretical Guarantees Safeguarded learned convex optimization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:46.010199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ff38687-2e22-4fd3-84a8-8d2a3eb2f2ec · outbound

This paper cites Proof of convergence for the proximal point al- gorithm.

Towards Robust Learning to Optimize with Theoretical Guarantees Proof of convergence for the proximal point al- gorithm

Reference 10

Resolution: verified exact; raw_fallback, observed 2026-08-07T00:25:39.720600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 66b65831-23a9-47a4-ad86-1165392398a9 · outbound

This paper cites Iterative Algorithm Induced Deep- Unfolding Neural Networks: Precoding Design for Mul- tiuser MIMO Systems.

Towards Robust Learning to Optimize with Theoretical Guarantees Iterative Algorithm Induced Deep- Unfolding Neural Networks: Precoding Design for Mul- tiuser MIMO Systems

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:45.851084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c1ab93cf-8126-4e47-b550-0a687eedcb8e · outbound

This paper cites Kalman and S.C.

Towards Robust Learning to Optimize with Theoretical Guarantees Kalman and S.C

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:45.634867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8914988a-017e-4136-8b25-9ad14a20cbfc · outbound

This paper cites A method for stochastic optimization.

Towards Robust Learning to Optimize with Theoretical Guarantees A method for stochastic optimization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:45.459193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e99f992c-ec5a-4a2f-9a0a-f102066c2215 · outbound

This paper cites Towards Constituting Mathematical Structures for Learning to Optimize.

Towards Robust Learning to Optimize with Theoretical Guarantees Towards Constituting Mathematical Structures for Learning to Optimize

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:45.247234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72d5122e-83cb-42d0-b1e5-5d9cb3dd8760 · outbound

This paper cites Learning gradient descent: Better generalization and longer horizons.

Towards Robust Learning to Optimize with Theoretical Guarantees Learning gradient descent: Better generalization and longer horizons

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:45.018914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 815451f4-487e-4e51-9dd2-1c4488aee8a2 · outbound

This paper cites Learning gradient descent: Better generalization and longer horizons.

Towards Robust Learning to Optimize with Theoretical Guarantees Learning gradient descent: Better generalization and longer horizons

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:44.797921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e327f22a-6f3c-4ffa-8d36-a8cd175c09e5 · outbound

This paper cites The generalized sigmoid activation func- tion: Competitive supervised learning.

Towards Robust Learning to Optimize with Theoretical Guarantees The generalized sigmoid activation func- tion: Competitive supervised learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:44.543308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 917e0046-e878-4fa7-89be-d5f6175e104f · outbound

This paper cites Monotone operators and the proximal point algorithm.

Towards Robust Learning to Optimize with Theoretical Guarantees Monotone operators and the proximal point algorithm

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:44.328877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3779ace9-ec79-421e-8994-d2395df8d281 · outbound

This paper cites An overview of gradient descent optimization algorithms.

Towards Robust Learning to Optimize with Theoretical Guarantees An overview of gradient descent optimization algorithms

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T00:25:36.242961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a608095f-6bda-45dc-bec4-2fdeb893b975 · outbound

This paper cites an unresolved cited work.

Towards Robust Learning to Optimize with Theoretical Guarantees Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:25:44.149529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1cd8b78d-8ea3-48bb-8c18-eea9f390d0f0 · outbound

This paper cites Coordinated Sum- Rate Maximization in Multicell MU-MIMO With Deep Un- rolling.

Towards Robust Learning to Optimize with Theoretical Guarantees Coordinated Sum- Rate Maximization in Multicell MU-MIMO With Deep Un- rolling

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:43.959053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8621f347-30f7-4c92-bae0-983928b71026 · outbound

This paper cites an unresolved cited work.

Towards Robust Learning to Optimize with Theoretical Guarantees Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:25:43.739297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7dbde3cf-8e14-4876-9c31-795800713c08 · outbound

This paper cites Towards Out-Of-Distribution Generalization: A Survey.

Towards Robust Learning to Optimize with Theoretical Guarantees Towards Out-Of-Distribution Generalization: A Survey

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T00:25:36.611573Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeb703ca-2e3e-49a4-be93-a729ecc83311 · outbound

This paper cites Prac- tical bayesian optimization of machine learning algorithms.

Towards Robust Learning to Optimize with Theoretical Guarantees Prac- tical bayesian optimization of machine learning algorithms

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:43.533082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2bea536b-8360-489e-a90b-2c6d4d8696da · outbound

This paper cites Subgradient Methods.

Towards Robust Learning to Optimize with Theoretical Guarantees Subgradient Methods

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:43.282321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7d27990e-87ed-4f0a-822c-b62c4985a834 · outbound

This paper cites Learning to optimize: Training deep neural networks for interference management.

Towards Robust Learning to Optimize with Theoretical Guarantees Learning to optimize: Training deep neural networks for interference management

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:43.045536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fb49f6ae-5d03-49bb-bf41-e319bda20049 · outbound

This paper cites Lecture 6: September 12.

Towards Robust Learning to Optimize with Theoretical Guarantees Lecture 6: September 12

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:42.831163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef8dee43-dbba-4871-9810-f76ace1aca95 · outbound

This paper cites Vandenberghe.

Towards Robust Learning to Optimize with Theoretical Guarantees Vandenberghe

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:42.603738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b3fdb892-0cb2-46cb-87d0-a96a880f4c94 · outbound

This paper cites Learned optimizers that scale and generalize.

Towards Robust Learning to Optimize with Theoretical Guarantees Learned optimizers that scale and generalize

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:42.340964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 578c972d-d6a9-4aa7-a9b2-886aba2b8401 · outbound

This paper cites Learn- ing to Generalize Provably in Learning to Optimize.

Towards Robust Learning to Optimize with Theoretical Guarantees Learn- ing to Generalize Provably in Learning to Optimize

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:42.012852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 675868eb-55c2-4fa4-afbe-85818a67a194 · outbound

This paper cites Niemegeers, and Sonia M.Heemstra De Groot.

Towards Robust Learning to Optimize with Theoretical Guarantees Niemegeers, and Sonia M.Heemstra De Groot

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:41.835976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5e9a38be-a643-4c5f-9faf-256e19d859af · outbound

This paper cites On the Fenchel Duality between Strong Convexity and Lipschitz Continuous Gradient.

Towards Robust Learning to Optimize with Theoretical Guarantees On the Fenchel Duality between Strong Convexity and Lipschitz Continuous Gradient

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T00:25:37.710919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a30a417-be5c-4c9e-89cd-37438cbc6434 · outbound

This paper cites Learn- ing to beamform in heterogeneous massive MIMO networks.

Towards Robust Learning to Optimize with Theoretical Guarantees Learn- ing to beamform in heterogeneous massive MIMO networks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:41.701009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f2fa6c09-997f-469b-9304-4ef2e9b78c9c · outbound

This paper cites well-trained.

Towards Robust Learning to Optimize with Theoretical Guarantees well-trained

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:41.481649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 25ba5475-23c8-4204-94af-76812f65bbbe · outbound

This paper cites Similar to the results in the smooth case of main pages, we derive several theorems and corollaries on per iteration and multi-iteration convergence of the L2O model.

Towards Robust Learning to Optimize with Theoretical Guarantees Similar to the results in the smooth case of main pages, we derive several theorems and corollaries on per iteration and multi-iteration convergence of the L2O model

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:41.288629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b196d616-f9be-4b34-b0f9-00370e768b47 · outbound

This paper cites Based on the definition, r(x) is proper and convex, where the “proper” means r(x) is trivially solvable for any x.

Towards Robust Learning to Optimize with Theoretical Guarantees Based on the definition, r(x) is proper and convex, where the “proper” means r(x) is trivially solvable for any x

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:41.128947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e9e08bbb-7699-49c5-bead-0c190b7c2be0 · outbound

This paper cites an unresolved cited work.

Towards Robust Learning to Optimize with Theoretical Guarantees Unresolved cited work

Reference 37

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:25:40.895063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 48ddfe4e-7578-4ab2-8c45-59d58ceeabfc · outbound

This paper cites The gradient-based longer horizon modeling method is more robust in OOD scenarios.

Towards Robust Learning to Optimize with Theoretical Guarantees The gradient-based longer horizon modeling method is more robust in OOD scenarios

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:40.712371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d08af1be-cb3d-4810-8aa6-0998fdd9716d · outbound

This paper cites By setting C g 4 ≤ C v 4 /(L2), the gradient-based longer horizon modeling method is more robust in OOD scenarios.

Towards Robust Learning to Optimize with Theoretical Guarantees By setting C g 4 ≤ C v 4 /(L2), the gradient-based longer horizon modeling method is more robust in OOD scenarios

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:40.542202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 63d88d5f-1675-457d-bff9-efa2f9e5b41b · outbound

This paper cites Implementation Details Our implementation is conducted with PyTorch based on the open-source code provided by the official implementation of.

Towards Robust Learning to Optimize with Theoretical Guarantees Implementation Details Our implementation is conducted with PyTorch based on the open-source code provided by the official implementation of

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:40.341827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b67304dd-5414-4dff-8ae6-7084f30def7d · outbound

This paper cites BP Frequency.

Towards Robust Learning to Optimize with Theoretical Guarantees BP Frequency

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:40.192055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8c28fde5-fb9a-463d-a351-28f964393f1c · outbound

This paper cites 1,000 patches are chosen from the BSDS500 dataset.

Towards Robust Learning to Optimize with Theoretical Guarantees 1,000 patches are chosen from the BSDS500 dataset

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:40.020828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f599ddb1-6b5c-477e-baec-e8beb3445458 · outbound

This paper cites Ionoshpere dataset contains 4,601 ai, bi ∈ R34 for each sample.

Towards Robust Learning to Optimize with Theoretical Guarantees Ionoshpere dataset contains 4,601 ai, bi ∈ R34 for each sample

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:25:39.868298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.