Paper Citation Record · LEDGER

On the Gradient Complexity of Private Optimization with Private Oracles

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2511.13999.

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

pith.paper-citation-record.v1
2511.13999 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T21:48:25.160427Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T18:15:19.879507Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T20:36:12.305666Z

Reference resolution

57 of 57 outbound references displayed

verified exact3
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 367fbbed-b799-430d-86a5-52abe6d10fe1 · outbound

This paper cites Differentially private generalized linear models revisited.

On the Gradient Complexity of Private Optimization with Private Oracles Differentially private generalized linear models revisited

Reference 1

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Observation 3dc735e5-0897-4d85-954f-0e21784ccce1 · outbound

This paper cites Faster rates of convergence to stationary points in differentially private optimization.

On the Gradient Complexity of Private Optimization with Private Oracles Faster rates of convergence to stationary points in differentially private optimization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:18.923973Z

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Observation 24e8b26f-b81d-4992-965a-0621a4dc6a33 · outbound

This paper cites Bartlett, Pradeep Ravikumar, and Martin J.

On the Gradient Complexity of Private Optimization with Private Oracles Bartlett, Pradeep Ravikumar, and Martin J

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:19.084877Z

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Observation 19ff2fd9-7a8f-409e-8e49-af2d3af7d3e8 · outbound

This paper cites Deep learning with differential privacy.

On the Gradient Complexity of Private Optimization with Private Oracles Deep learning with differential privacy

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:19.232381Z

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Observation 08c9f521-7a6b-46bb-af2f-1f3a00b7d8fb · outbound

This paper cites Information-constrained optimization: can adaptive processing of gradients help? In M.

On the Gradient Complexity of Private Optimization with Private Oracles Information-constrained optimization: can adaptive processing of gradients help? In M

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:19.458534Z

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Observation d11c1208-693b-4336-b1f0-14f4092cddf9 · outbound

This paper cites Private stochastic convex optimization: Optimal rates in l1 geometry.

On the Gradient Complexity of Private Optimization with Private Oracles Private stochastic convex optimization: Optimal rates in l1 geometry

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:19.611482Z

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Observation 2bee1cd7-7afe-476b-b074-458021bdce0d · outbound

This paper cites Qsgd: Communication-efficient sgd via gradient quantization and encoding.

On the Gradient Complexity of Private Optimization with Private Oracles Qsgd: Communication-efficient sgd via gradient quantization and encoding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:19.773500Z

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Observation a8f1033c-0024-46f4-be6b-da217f62489b · outbound

This paper cites Communication complexity of distributed convex learning and optimization.

On the Gradient Complexity of Private Optimization with Private Oracles Communication complexity of distributed convex learning and optimization

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:19.937275Z

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Observation 21336621-340a-4487-bfa1-c0d6ed0959c7 · outbound

This paper cites Privacy amplification by subsampling: Tight analyses via couplings and divergences.

On the Gradient Complexity of Private Optimization with Private Oracles Privacy amplification by subsampling: Tight analyses via couplings and divergences

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:20.133527Z

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Observation d4a1d4da-0f9d-4e2d-ae15-348f889b6d4a · outbound

This paper cites Rothblum, and Thomas Steinke.

On the Gradient Complexity of Private Optimization with Private Oracles Rothblum, and Thomas Steinke

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:20.269265Z

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Observation e182274a-e815-413c-98d8-19f4ae9e8cd8 · outbound

This paper cites Stability and generalization.

On the Gradient Complexity of Private Optimization with Private Oracles Stability and generalization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:20.316881Z

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Observation a6d4019e-b84f-45d0-ae0f-0ca27e3dde82 · outbound

This paper cites Stability of stochastic gradient descent on nonsmooth convex losses.

On the Gradient Complexity of Private Optimization with Private Oracles Stability of stochastic gradient descent on nonsmooth convex losses

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:20.342888Z

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Observation 1962cf5a-1176-4a77-b274-a140ddbbd81d · outbound

This paper cites Private stochastic convex optimization with optimal rates.

On the Gradient Complexity of Private Optimization with Private Oracles Private stochastic convex optimization with optimal rates

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:20.413111Z

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Observation ea5c2352-37ac-4624-9b75-56152d2f6910 · outbound

This paper cites Differentially private algorithms for the stochastic saddle point problem with optimal rates for the strong gap.

On the Gradient Complexity of Private Optimization with Private Oracles Differentially private algorithms for the stochastic saddle point problem with optimal rates for the strong gap

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:20.486940Z

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Observation 1a727bf7-3d25-42b8-b1aa-b411826d32e3 · outbound

This paper cites Private algorithms for stochastic saddle points and variational inequalities: Beyond euclidean geometry.

On the Gradient Complexity of Private Optimization with Private Oracles Private algorithms for stochastic saddle points and variational inequalities: Beyond euclidean geometry

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:20.593921Z

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Observation 494ab03f-229c-499d-98b6-00b3d31828f4 · outbound

This paper cites Lower bounds on the oracle complexity of nonsmooth convex optimization via information theory.

On the Gradient Complexity of Private Optimization with Private Oracles Lower bounds on the oracle complexity of nonsmooth convex optimization via information theory

Reference 16

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Observation 46a47cf2-b3c1-40e2-a17c-8ede2e8e0561 · outbound

This paper cites Complexity of highly parallel non-smooth convex optimization.

On the Gradient Complexity of Private Optimization with Private Oracles Complexity of highly parallel non-smooth convex optimization

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:20.768412Z

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Observation 5db9729a-937b-4eb7-ac6a-4969adb4b302 · outbound

This paper cites Differentially Private Release and Learning of Threshold Functions.

On the Gradient Complexity of Private Optimization with Private Oracles Differentially Private Release and Learning of Threshold Functions

Reference 18

Resolution: verified exact; doi, observed 2026-08-03T21:54:06.606603Z

Source-reported events for the cited work

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Observation 930ba145-43ab-4241-9618-4a4b1d69b7ba · outbound

This paper cites Concentrated differential privacy: Simplifications, extensions, and lower bounds.

On the Gradient Complexity of Private Optimization with Private Oracles Concentrated differential privacy: Simplifications, extensions, and lower bounds

Reference 19

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Observation 0110e2d2-c5c1-459d-a862-1a651691b49f · outbound

This paper cites Private empirical risk minimization: Efficient algorithms and tight error bounds.

On the Gradient Complexity of Private Optimization with Private Oracles Private empirical risk minimization: Efficient algorithms and tight error bounds

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:21.065154Z

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Observation 6ced245a-dc5a-4456-92e8-8295280647f6 · outbound

This paper cites Fingerprinting codes and the price of approximate differential privacy.

On the Gradient Complexity of Private Optimization with Private Oracles Fingerprinting codes and the price of approximate differential privacy

Reference 21

Resolution: verified exact; doi, observed 2026-08-03T21:54:06.399255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 03b5fb4a-3107-423c-9a60-ee04cd40f13e · outbound

This paper cites Choquette-Choo, Arun Ganesh, and Abhradeep Guha Thakurta.

On the Gradient Complexity of Private Optimization with Private Oracles Choquette-Choo, Arun Ganesh, and Abhradeep Guha Thakurta

Reference 22

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Observation c0014d3e-63f2-445e-a7fb-49498f22b3d6 · outbound

This paper cites Choquette-Choo, H.

On the Gradient Complexity of Private Optimization with Private Oracles Choquette-Choo, H

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:21.483605Z

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Observation 4a2aa61e-7c01-4194-8723-0a5d2e7d41be · outbound

This paper cites Advancing Differential Privacy : Where We Are Now and Future Directions for Real - World Deployment.

On the Gradient Complexity of Private Optimization with Private Oracles Advancing Differential Privacy : Where We Are Now and Future Directions for Real - World Deployment

Reference 24

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Observation 3f7130cb-7921-4353-9478-651e7d8a2531 · outbound

This paper cites Resqueing parallel and private stochastic convex optimization.

On the Gradient Complexity of Private Optimization with Private Oracles Resqueing parallel and private stochastic convex optimization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:21.607968Z

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Observation e15eccc7-0d53-4ad0-a4f7-af3e8c0bb4e7 · outbound

This paper cites Differentially private empirical risk minimization.

On the Gradient Complexity of Private Optimization with Private Oracles Differentially private empirical risk minimization

Reference 26

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Observation 54e0869a-2d56-4b7d-8fe3-7e6c04b4c391 · outbound

This paper cites Cover and Joy A.

On the Gradient Complexity of Private Optimization with Private Oracles Cover and Joy A

Reference 27

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Observation d5a27e6f-1031-4cc7-9a67-1ed6da035fb2 · outbound

This paper cites Calibrating noise to sensitivity in private data analysis.

On the Gradient Complexity of Private Optimization with Private Oracles Calibrating noise to sensitivity in private data analysis

Reference 28

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Observation ecdbcbdd-2d25-4588-8a98-26d1e139b991 · outbound

This paper cites Robust traceability from trace amounts.

On the Gradient Complexity of Private Optimization with Private Oracles Robust traceability from trace amounts

Reference 29

Resolution: verified exact; doi, observed 2026-08-03T21:54:06.228621Z

Source-reported events for the cited work

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Observation e695bfc3-2646-4dc7-8cda-ffe2c05ce22c · outbound

This paper cites Distance-based and continuum Fano inequalities with applications to statistical estimation.

On the Gradient Complexity of Private Optimization with Private Oracles Distance-based and continuum Fano inequalities with applications to statistical estimation

Reference 30

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Observation 6c6f11d5-e6c9-43a0-841f-f9f99c481340 · outbound

This paper cites Private stochastic convex optimization: optimal rates in linear time.

On the Gradient Complexity of Private Optimization with Private Oracles Private stochastic convex optimization: optimal rates in linear time

Reference 31

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Observation f0c409ee-d64e-4745-80f1-9d4b6e6b78d4 · outbound

This paper cites Roy, and Ali Ramezani-Kebrya.

On the Gradient Complexity of Private Optimization with Private Oracles Roy, and Ali Ramezani-Kebrya

Reference 32

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Observation efc83629-c5e5-4b7f-a0f4-c48f25d89930 · outbound

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On the Gradient Complexity of Private Optimization with Private Oracles Private convex optimization via exponential mechanism

Reference 33

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Observation ff9e549a-f4a8-4c51-a159-361715c84da3 · outbound

This paper cites Lower bounds and nearly optimal algorithms in distributed learning with communication compression.

On the Gradient Complexity of Private Optimization with Private Oracles Lower bounds and nearly optimal algorithms in distributed learning with communication compression

Reference 34

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Observation beddd7c6-08bc-42eb-b789-01be119d761f · outbound

This paper cites Train faster, generalize better: stability of stochastic gradient descent.

On the Gradient Complexity of Private Optimization with Private Oracles Train faster, generalize better: stability of stochastic gradient descent

Reference 35

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Observation 89cfe399-aa39-4d80-b4ec-6bb4983a9374 · outbound

This paper cites Accelerating stochastic gradient descent using predictive variance reduction.

On the Gradient Complexity of Private Optimization with Private Oracles Accelerating stochastic gradient descent using predictive variance reduction

Reference 36

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Observation a11ee421-42b7-48b9-a2d6-42e588a6cbc3 · outbound

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On the Gradient Complexity of Private Optimization with Private Oracles Private non-smooth erm and sco in subquadratic steps

Reference 37

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Observation 5b5101f1-141e-4263-9764-3df95432d35b · outbound

This paper cites Brendan McMahan.

On the Gradient Complexity of Private Optimization with Private Oracles Brendan McMahan

Reference 38

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On the Gradient Complexity of Private Optimization with Private Oracles Practical and private (deep) learning without sampling or shuffling

Reference 39

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This paper cites Private federated learning without a trusted server: Optimal algorithms for convex losses.

On the Gradient Complexity of Private Optimization with Private Oracles Private federated learning without a trusted server: Optimal algorithms for convex losses

Reference 40

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Observation 6fb2a30a-7185-45f1-b7fa-ab6352c18684 · outbound

This paper cites Limits on gradient compression for stochastic optimization.

On the Gradient Complexity of Private Optimization with Private Oracles Limits on gradient compression for stochastic optimization

Reference 41

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Observation 59e5a10b-4cb1-45fc-a807-ea095e6149ae · outbound

This paper cites Ratq: A universal fixed-length quantizer for stochastic optimization.

On the Gradient Complexity of Private Optimization with Private Oracles Ratq: A universal fixed-length quantizer for stochastic optimization

Reference 42

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Observation 5727be26-da96-494e-afc8-94398f20e0ac · outbound

This paper cites Differentially private non-convex optimization under the kl condition with optimal rates.

On the Gradient Complexity of Private Optimization with Private Oracles Differentially private non-convex optimization under the kl condition with optimal rates

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:23.514219Z

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Observation 8e44c610-0d3e-479f-8e53-1c21da3dbce2 · outbound

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On the Gradient Complexity of Private Optimization with Private Oracles Unresolved cited work

Reference 44

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Observation f5667ee7-f232-428b-85c7-7cce5e22fcba · outbound

This paper cites How to dp-fy ml: A practical tutorial to machine learning with differential privacy.

On the Gradient Complexity of Private Optimization with Private Oracles How to dp-fy ml: A practical tutorial to machine learning with differential privacy

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:23.774643Z

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On the Gradient Complexity of Private Optimization with Private Oracles a is\" a , Joonas J\

Reference 46

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On the Gradient Complexity of Private Optimization with Private Oracles Optimal convergence rates for convex distributed optimization in networks

Reference 47

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This paper cites Stich, Jean-Baptiste Cordonnier, and Martin Jaggi.

On the Gradient Complexity of Private Optimization with Private Oracles Stich, Jean-Baptiste Cordonnier, and Martin Jaggi

Reference 48

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This paper cites Characterizing the accuracy-communication-privacy trade-off in distributed stochastic convex optimization.

On the Gradient Complexity of Private Optimization with Private Oracles Characterizing the accuracy-communication-privacy trade-off in distributed stochastic convex optimization

Reference 49

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On the Gradient Complexity of Private Optimization with Private Oracles Understanding machine learning: From theory to algorithms

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:24.340434Z

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On the Gradient Complexity of Private Optimization with Private Oracles Public-data assisted private stochastic optimization: Power and limitations

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:24.446786Z

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On the Gradient Complexity of Private Optimization with Private Oracles High-Dimensional Probability: An Introduction with Applications in Data Science

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:24.491393Z

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This paper cites The min-max complexity of distributed stochastic convex optimization with intermittent communication.

On the Gradient Complexity of Private Optimization with Private Oracles The min-max complexity of distributed stochastic convex optimization with intermittent communication

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:24.601590Z

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Observation 2d906b5d-d2ff-4298-8eeb-eae7416125d7 · outbound

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On the Gradient Complexity of Private Optimization with Private Oracles Tight complexity bounds for optimizing composite objectives

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:24.793828Z

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Observation ecc12186-4656-4e58-90fc-c25c5d69d2fb · outbound

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On the Gradient Complexity of Private Optimization with Private Oracles Communication compression techniques in distributed deep learning: A survey

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:24.866088Z

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Observation f12d8a2a-fc2c-40f9-95c9-60846720b50e · outbound

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On the Gradient Complexity of Private Optimization with Private Oracles Gradient perturbation is underrated for differentially private convex optimization

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:24.937642Z

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Observation 42210c96-378b-4459-8ec1-112c845b991a · outbound

This paper cites Bring your own algorithm for optimal differentially private stochastic minimax optimization.

On the Gradient Complexity of Private Optimization with Private Oracles Bring your own algorithm for optimal differentially private stochastic minimax optimization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T21:48:25.160427Z

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Pith citing papers

Observation 8c9ef854-4019-412e-86c1-b3a73ec2a6e6 · inbound

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation cites this paper.

Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation On the Gradient Complexity of Private Optimization with Private Oracles

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-10T02:19:33.747884Z

Source-reported events for the cited work

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