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Paper Citation Record · LEDGER

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition

As of 8 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2509.04668.

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

pith.paper-citation-record.v1
2509.04668 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T06:03:55.593835Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

61 of 61 outbound references displayed

  • verified exact5
  • verified fuzzy50
  • unresolved6
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f5b51b0b-44fe-4752-99ca-2b2fdbdf41c6 · outbound

This paper cites Deep learning with differential privacy.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Deep learning with differential privacy

Reference 1

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T06:03:45.914758Z digest=sha256:f6479cb57838062f14e48c99ba49519dbb71f15e19d2178567085dac60460c96

Observation e819a4fa-e76d-4ed6-94cc-547c0c39ba41 · outbound

This paper cites Differentially private assouad, fano, and le cam.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially private assouad, fano, and le cam

Reference 2

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.078285Z digest=sha256:e5c01ff2ef20f9730beac8f5d4c9e586a933405bf37437cb5b60f91dd783bafa

Observation 7605ccf0-b105-42b7-b7ae-30dd3a57fe4b · outbound

This paper cites Private adaptive gradient methods for convex optimization.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private adaptive gradient methods for convex optimization

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.199802Z digest=sha256:e314c15a53d9a256e24934b49aa766e4fc7e7f6bcdb6e466528f1dcaa31d4117

Observation 6cc6b33a-46ec-40d6-ae07-3b17ea4e30a9 · outbound

This paper cites Adapting to function difficulty and growth conditions in private optimization.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Adapting to function difficulty and growth conditions in private optimization

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.375344Z digest=sha256:f048492b27c4b230b54759b45adecbefd78e145c05fcbb2ec92f0e1a15b79cc3

Observation 8dc47ccb-f724-440d-96e1-71096c608fa4 · outbound

This paper cites Private stochastic convex optimization with heavy tails: Near-optimality from simple reductions.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private stochastic convex optimization with heavy tails: Near-optimality from simple reductions

Reference 5

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.514313Z digest=sha256:efe0e720b12185b2c9ae489dd500ad912483b5c35364679ee462df15a5a51f9b

Observation a1afcc1d-bec8-41bc-a27e-46ef5b0409b9 · outbound

This paper cites Privacy and Statistical Risk: Formalisms and Minimax Bounds.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Privacy and Statistical Risk: Formalisms and Minimax Bounds

Reference 6

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unresolved
no resolver link, observed 2026-08-05T06:03:46.684887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T06:03:46.684887Z digest=sha256:9e7dbec60051d1b5c87f621b530db8117d8b41834f0670a89e3de05e732e8580

Observation c2ce5d31-4a52-485f-968e-1a68c42abfad · outbound

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

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private empirical risk minimization: Efficient algorithms and tight error bounds

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:11.603495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.905433Z digest=sha256:12e5934a4f89ee74135feebb5e10522cc288aceb9ed24914bb8498e7cd2cc04b

Observation ffc5892d-4e66-427c-b576-56a9395e114e · outbound

This paper cites Private Stochastic Convex Optimization with Optimal Rates.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private Stochastic Convex Optimization with Optimal Rates

Reference 8

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unresolved
no resolver link, observed 2026-08-05T06:03:47.054748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T06:03:47.054748Z digest=sha256:2e5fa28af9de2fd2b13cd1ea6b503867d18752eec57ff0f08a3513a087c6ee50

Observation 8d34f187-6b6f-415a-9410-af80b8e85c4f · outbound

This paper cites Statistical advances in the biomedical science.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Statistical advances in the biomedical science

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:11.374823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:47.374782Z digest=sha256:0b79ece103bfe02d4d3ca70fc2adb9546ef7b42afc22ec2ded0a6cf304ef8960

Observation b8ae2518-6d0f-4cc3-9ccc-757e5b1eea4d · outbound

This paper cites A method for finding projections onto the intersection of convex sets in hilbert spaces.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition A method for finding projections onto the intersection of convex sets in hilbert spaces

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:11.124383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:47.604746Z digest=sha256:408a08bc73299d59ced266256a0d911c3e4cc19391830a53b4208d272194a120

Observation 39b436ce-1d21-40e9-b24c-0fe69a302e8a · outbound

This paper cites Propose, Test, Release: Differentially private estimation with high probability.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Propose, Test, Release: Differentially private estimation with high probability

Reference 11

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unresolved
no resolver link, observed 2026-08-05T06:03:47.764742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T06:03:47.764742Z digest=sha256:c3b89accd8cdcabd6454e215d1942e206954eee9a1bcb317d0babec019e2d4dd

Observation 5276250d-bfe5-4e1d-97cb-a935a81bfe3f · outbound

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

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Concentrated differential privacy: Simplifications, extensions, and lower bounds

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:10.804248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:47.924799Z digest=sha256:535807dcbe0cc02fef01347af80e06e3ebc61d19418324ccb4e4755ef1295020

Observation 39a9f412-ffe4-4e74-8c20-b443433b7013 · outbound

This paper cites Differentially private empirical risk minimization.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially private empirical risk minimization

Reference 13

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unresolved
no resolver link, observed 2026-08-05T06:03:48.069501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T06:03:48.069501Z digest=sha256:10d4637c037e80fdaf2537bc5b06b88efcac326f943f7384a9835dc721ecf68c

Observation 370fb437-e22f-45da-9eff-45f948acc959 · outbound

This paper cites Quantizing heavy-tailed data in statistical estimation:(near) minimax rates, covariate quantization, and uniform recovery.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Quantizing heavy-tailed data in statistical estimation:(near) minimax rates, covariate quantization, and uniform recovery

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.184768Z digest=sha256:aee7437db77576c04b707a7be80607e06e267c2af53a751b88c49a8c1ec305c1

Observation 678dcfa1-56c1-477c-8673-7aa0bb6ea7d7 · outbound

This paper cites Revisiting differentially private relu regression.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Revisiting differentially private relu regression

Reference 15

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.299516Z digest=sha256:d27899f846d73d461cb736142a46f77cf5b707e016bd7f9f63ca782ed0ae70df

Observation ac88e871-ec3f-4b99-bfa4-e74a6d66aaf5 · outbound

This paper cites Nearly optimal differentially private relu regression.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Nearly optimal differentially private relu regression

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.467221Z digest=sha256:a36aad93e84df6df99967a9250dacc74ab6ac34b15e3c38489f7909dcebc5483

Observation 81eb4345-0ed9-4d52-8190-7d25d4c8ba9e · outbound

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

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Calibrating noise to sensitivity in private data analysis

Reference 17

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.604740Z digest=sha256:593a5bd9c54458172eff902d3f99bdf1f9cd9861ed5df190058ff8feed09a58f

Observation 03faef9c-93db-4b44-a266-83a6286ece0e · outbound

This paper cites An algorithm for restricted least squares regression.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition An algorithm for restricted least squares regression

Reference 18

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.844805Z digest=sha256:a5cf5558151e2ff7401618f8f27ea77ff546ec7182419adbb6550f9f68f37316

Observation 0b85ac43-45a5-44ff-9b81-1f7f9c9a5425 · outbound

This paper cites High probability generalization bounds for uniformly stable algorithms with nearly optimal rate.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition High probability generalization bounds for uniformly stable algorithms with nearly optimal rate

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.077030Z digest=sha256:edea06c105d745467c81c882197991c2952ea2191bd7ed308120d16526aac96c

Observation df8a2c65-a7a0-42a8-8644-b41bc357d84a · outbound

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

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private stochastic convex optimization: optimal rates in linear time

Reference 20

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.203508Z digest=sha256:646085bfec5ab6b90ba7d37b2cb9a9e812b2c36c39fee1ce659e2bccec33936b

Observation c904bd72-8712-4326-8e39-b83303c75fc2 · outbound

This paper cites Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling

Reference 21

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raw_fallback, observed 2026-08-05T06:04:08.416000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.330505Z digest=sha256:e489a4b7a15b9ac3bd0fad169424db974077e4873cbec874f974728df8e8bdac

Observation 915791e3-9ba0-42ce-881b-8683d4b2233d · outbound

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

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Train faster, generalize better: Stability of stochastic gradient descent

Reference 22

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raw_fallback, observed 2026-08-05T06:04:08.154747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.454835Z digest=sha256:c64f2c63c661994b0fcdd17c98c565c0831b7469797d4ea6750eaddf7dc369f5

Observation 5064ad7d-0c1d-4a00-8b7c-0c8b62cc9035 · outbound

This paper cites High dimensional differentially private stochastic optimization with heavy-tailed data.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition High dimensional differentially private stochastic optimization with heavy-tailed data

Reference 23

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raw_fallback, observed 2026-08-05T06:04:07.825751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.624740Z digest=sha256:485c368839213900f9d5925ffb8fcc26eb3c9313c36d9644c51d5a14937ebdc2

Observation 19d075c1-1f51-4ece-9b82-c980f1625946 · outbound

This paper cites Pairwise learning with differential privacy guarantees.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Pairwise learning with differential privacy guarantees

Reference 24

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raw_fallback, observed 2026-08-05T06:04:07.654838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.784748Z digest=sha256:6fd2b42a2c72d49e330fa6d935e078ce9954b9e394a3fbfd3220d06574694cd9

Observation fbf1bd5e-f442-4e85-ac35-4e56fde5cb5a · outbound

This paper cites Heavy-tailed distributions and robustness in economics and finance, volume 214.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Heavy-tailed distributions and robustness in economics and finance, volume 214

Reference 25

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raw_fallback, observed 2026-08-05T06:04:07.364836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.904468Z digest=sha256:6adbf0707bc5b697cf24ca45de50a4d07d860561ce57863d847e9228a35030cd

Observation 2a65e795-cc34-4d55-a9bf-fe444e73af38 · outbound

This paper cites Private mean estimation of heavy-tailed distributions.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private mean estimation of heavy-tailed distributions

Reference 26

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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:07.106243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.114339Z digest=sha256:f592be7ba95750c9f972947887f0a15d5b579498571dc6a03e956a0940a4c531

Observation e10d8172-ac3b-478a-8a77-f05b1c7df2d8 · outbound

This paper cites Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data

Reference 27

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local_arxiv, observed 2026-08-05T06:03:57.000646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.247839Z digest=sha256:1e4d5c98fe5f5498e67b645b0e07e0833ef9354fa655d44951c117c5421e120d

Observation d7ace8a1-6dd8-48e3-9c8d-da8d92f96342 · outbound

This paper cites Improved rates for differentially private stochastic convex optimization with heavy-tailed data.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Improved rates for differentially private stochastic convex optimization with heavy-tailed data

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:06.765504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.474744Z digest=sha256:722d7e0452954c516f7abfcad76d68b6da5a6a69d33c102777d678997a18b6b5

Observation eb12841a-7dd8-4f0a-b3b9-99dc44261d35 · outbound

This paper cites Linear convergence of gradient and proximal-gradient methods under the polyak- ojasiewicz condition.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Linear convergence of gradient and proximal-gradient methods under the polyak- ojasiewicz condition

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:06.426471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.744742Z digest=sha256:8e3c17767f081a21ba739f16693ea2bbb47bfac68320a0b79ea0aea50b166381

Observation 28fb8aad-4028-4114-a135-78605cf1cca0 · outbound

This paper cites Efficient private empirical risk minimization for high-dimensional learning.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Efficient private empirical risk minimization for high-dimensional learning

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:06.078584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.904747Z digest=sha256:3089c31bf5ad7dab307e43aff6ed5624e9d577acc483f92064cf53a230433cf0

Observation 712a3ba2-7960-4e4a-bec4-5f6680995fae · outbound

This paper cites Private convex empirical risk minimization and high-dimensional regression.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private convex empirical risk minimization and high-dimensional regression

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:05.845030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.066096Z digest=sha256:1f9bc43d9afcaf7b9d1df657e9d75109fdf24c366bb77faf5e39c8f65b3e4d44

Observation d8792616-a54f-444e-861d-2035ddd86d64 · outbound

This paper cites Fast rates for exp-concave empirical risk minimization.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Fast rates for exp-concave empirical risk minimization

Reference 32

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raw_fallback, observed 2026-08-05T06:04:05.538006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.216553Z digest=sha256:a3602c11d85ed219b3a66729e3a6d2845fc3b33bed9b3bec3a26a98804cfa572

Observation c3956056-6b48-4d9f-a97f-3a93868978d4 · outbound

This paper cites Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions

Reference 33

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local_arxiv, observed 2026-08-05T06:03:56.714100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.337405Z digest=sha256:e720702fddba54b1f1a7f5767f38b7acbd4304efb8eb17b000729b6c3b1aa312

Observation 798e9e8d-ae27-490f-8fa8-436745272eee · outbound

This paper cites Robust and Differentially Private Mean Estimation.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Robust and Differentially Private Mean Estimation

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-08-05T06:03:56.408995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.524746Z digest=sha256:145498210c81d8ec2538b0ddf682fef4431f9d889e10f80faf4c802fc33449e3

Observation 6283abe3-b35e-4c7d-869b-b21910e739d4 · outbound

This paper cites Private stochastic optimization with large worst-case lipschitz parameter: Optimal rates for (non-smooth) convex losses and extension to non-convex losses.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private stochastic optimization with large worst-case lipschitz parameter: Optimal rates for (non-smooth) convex losses and extension to non-convex losses

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:05.224235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.669011Z digest=sha256:6b33250d4a6f8cbf7c1893bb0fdcd1d21f155ef9a8bae9797bc503dea369d752

Observation 9713e4b7-b1d9-4510-8b98-b7c0940a2771 · outbound

This paper cites Privacy integrated queries: an extensible platform for privacy-preserving data analysis.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Privacy integrated queries: an extensible platform for privacy-preserving data analysis

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:04.884087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.800697Z digest=sha256:8b936c185fef7fecb39b609ed19a4566a136adc7d38f5f6f7a20e3edcc1c75ce

Observation dbe9d191-ce08-46a7-9959-0869070e5985 · outbound

This paper cites Optimal rates for first-order stochastic convex optimization under Tsybakov noise condition.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Optimal rates for first-order stochastic convex optimization under Tsybakov noise condition

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-08-05T06:03:56.150376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.973057Z digest=sha256:314d6e2d67ad5f36476742bfd947106e0cfd234be0ff1282f0c4e6a20ed879ba

Observation 8f90b9ed-7d05-4965-b58a-9d732c0d7ee3 · outbound

This paper cites Algorithmic connections between active learning and stochastic convex optimization.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Algorithmic connections between active learning and stochastic convex optimization

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:04.610716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.183004Z digest=sha256:4b18b0dfbc9dcaab5560a349a98949d5a342896824c7cf543792d0b4dcafd8bc

Observation 43831581-8bf7-44cb-8cad-05abfa5b92c9 · outbound

This paper cites Is interaction necessary for distributed private learning? In 2017 IEEE Symposium on Security and Privacy (SP), pp.\ 58--77.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Is interaction necessary for distributed private learning? In 2017 IEEE Symposium on Security and Privacy (SP), pp.\ 58--77

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:04.314743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.304745Z digest=sha256:b1f70985fcdd5b456badb610bb2f57e3f4ca1756b943bf1dc5c5ea155f01e401

Observation 89466734-2a05-4c96-9126-4ab705569812 · outbound

This paper cites Faster rates of private stochastic convex optimization.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Faster rates of private stochastic convex optimization

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:03.995851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.419182Z digest=sha256:d243ab16a920edb244d08aea7ccf52ccc9211797f8788823e2bca4d3aeb70aae

Observation f8add3e9-1254-446b-90c3-7f9f01276d71 · outbound

This paper cites Differentially private stochastic convex optimization in (non)-euclidean space revisited.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially private stochastic convex optimization in (non)-euclidean space revisited

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:03.722968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.541340Z digest=sha256:49941c47af046c9202b6b2a458663a01eb4cb2998f00bc592373e3151ee12979

Observation d4e1f629-f516-47ff-abe0-89738c8f5c63 · outbound

This paper cites Faster rates of differentially private stochastic convex optimization.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Faster rates of differentially private stochastic convex optimization

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:03.417125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.722187Z digest=sha256:7fb76ecc12c9b4adb1a4c1e0e3200523c6e1cf40d9c66557a2b9e4ef77b40d8a

Observation 5671799f-f6b6-477f-88ea-aca026363b15 · outbound

This paper cites Private stochastic convex optimization and sparse learning with heavy-tailed data revisited.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private stochastic convex optimization and sparse learning with heavy-tailed data revisited

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:03.094742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.848428Z digest=sha256:7f7caeca6946b11562b2ba7871799955c73c57e6c878d3a993ace92c3849f130

Observation b8cbc73c-cb15-4d6f-bff1-bb4ca7bc8fe2 · outbound

This paper cites Optimal rates of (locally) differentially private heavy-tailed multi-armed bandits.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Optimal rates of (locally) differentially private heavy-tailed multi-armed bandits

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:02.777956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.994741Z digest=sha256:8e315512bee662ad044d74ccee58c1318ee24510a92c207f7eb78f3be57a1e61

Observation c3143b29-2dcd-4713-9d6e-19fc7ed87db5 · outbound

This paper cites Differentially Private Sparse Linear Regression with Heavy-tailed Responses.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially Private Sparse Linear Regression with Heavy-tailed Responses

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-05T06:03:55.887247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.222767Z digest=sha256:9a4dd5037c77acec08452b19ca8ce8d87508c44a2a23200268628dc42bcb5c27

Observation 3bd38622-6120-40f8-8b5f-573e16187d78 · outbound

This paper cites Fast rates in statistical and online learning.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Fast rates in statistical and online learning

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:02.364755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.304862Z digest=sha256:48c9158d4f3a36bc0993c7a8b2bb9a2619a164ddfc99f558926524ca528b180c

Observation efa9d90e-c418-4787-98e3-e93caeba394a · outbound

This paper cites Differentially private _1 -norm linear regression with heavy-tailed data.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially private _1 -norm linear regression with heavy-tailed data

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:02.014936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.464879Z digest=sha256:d80c80d00c0f114246d99b5c4bd60b33087f11f44f7476985311133ac0ecf5a6

Observation eb614c0f-6a73-4a79-aff3-be6958ded636 · outbound

This paper cites Private least absolute deviations with heavy-tailed data.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Private least absolute deviations with heavy-tailed data

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:01.634749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.591328Z digest=sha256:2f61f257d15eb6faa696f3e84ead8b2eeeb7f7539ddce966f92308ef84453dd0

Observation 11ef6f1e-313c-4d7b-9e61-110f58f568d4 · outbound

This paper cites Differentially private empirical risk minimization revisited: Faster and more general.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially private empirical risk minimization revisited: Faster and more general

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:01.363145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.744129Z digest=sha256:5d9be4fb1e31f2053143b530a0e939bd5b7db2e057d8a91ff1a140132b7aa1f1

Observation 372b8de9-fa69-4179-a882-1a6b304d08de · outbound

This paper cites Empirical risk minimization in non-interactive local differential privacy revisited.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Empirical risk minimization in non-interactive local differential privacy revisited

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:00.974171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.934738Z digest=sha256:b9748d499337668242eebbe834449eccf299d187325154471c4590b08d4c4685

Observation 902933e3-3507-43ef-8e81-3e3384f49950 · outbound

This paper cites Differentially private empirical risk minimization with non-convex loss functions.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially private empirical risk minimization with non-convex loss functions

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:00.425168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.994740Z digest=sha256:5c00b31aa17e2b4ac0e5ed8a30e7a1859bcf3fe27432784bd8266b43a64274a9

Observation 425eec13-0ae7-4ad0-9ab4-739401c89ecf · outbound

This paper cites Noninteractive locally private learning of linear models via polynomial approximations.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Noninteractive locally private learning of linear models via polynomial approximations

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:04:00.115955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.146602Z digest=sha256:dfebcb4fa5d0f0cf1026f725d9fbd6d378102fb18c60e206ee857ef93b358e84

Observation ede49832-255d-47b6-976d-59346775c3b6 · outbound

This paper cites On differentially private stochastic convex optimization with heavy-tailed data.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition On differentially private stochastic convex optimization with heavy-tailed data

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:03:59.727517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.306833Z digest=sha256:99872b9867feead2a4faf1fecdb35fe1567f888adcc6810135a0d73906e3cf0c

Observation 6e353a7a-070d-4da7-86ed-0e17b8eff04d · outbound

This paper cites Statistical methods for the analysis of biomedical data, volume 371.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Statistical methods for the analysis of biomedical data, volume 371

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:03:59.385288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.422488Z digest=sha256:9954305d843c3fef286bad85bac1cb796262328023af90248488e67cbc2b115f

Observation cf67538d-e93b-4980-981f-8656005c9690 · outbound

This paper cites Bolt-on differential privacy for scalable stochastic gradient descent-based analytics.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Bolt-on differential privacy for scalable stochastic gradient descent-based analytics

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:03:58.984748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.604743Z digest=sha256:f3ea275558b80fdb07870774fa609f3ac5458a95d7ec2757b6dad08e78672743

Observation e9bd223e-24c4-4f07-8349-3967dc3ac2da · outbound

This paper cites Differentially private episodic reinforcement learning with heavy-tailed rewards.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially private episodic reinforcement learning with heavy-tailed rewards

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:03:58.718077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.849479Z digest=sha256:799a67b681022c5c045cfdcd578c7b7ed8c52838628855f53fcf962f67894e95

Observation 8d32a784-e39a-404a-a6b2-669b75a08c88 · outbound

This paper cites On private and robust bandits.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition On private and robust bandits

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:03:58.399488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:55.044880Z digest=sha256:82d61d9793ffc8c843719389b5514047467b1b5aeccd3bfd78c3d61c28a44bc5

Observation 46466ba9-6cb4-4f87-b292-5200975563ea · outbound

This paper cites Stochastic convex optimization: Faster local growth implies faster global convergence.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Stochastic convex optimization: Faster local growth implies faster global convergence

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:03:58.113061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:55.204740Z digest=sha256:9a255d07e07b19f43f2dac5b7cda2a4200e9375885e0929e1ba7751322b0dc3c

Observation 09562325-e18b-4f82-9fb7-8cbc50d5a620 · outbound

This paper cites Differentially private pairwise learning revisited.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition Differentially private pairwise learning revisited

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:03:57.805830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:55.286091Z digest=sha256:783c98cd391e0715cac9eb3d506f52271f6849935a96e98e340143d24af917a4

Observation d502aa98-5b3e-4c6e-9509-dae8e4b478fe · outbound

This paper cites A simple analysis for exp-concave empirical minimization with arbitrary convex regularizer.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition A simple analysis for exp-concave empirical minimization with arbitrary convex regularizer

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T06:03:57.456418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T06:03:55.499423Z digest=sha256:ba1e3a83057635e6cb16ded85125eb5deac43ecc0a3a9e90e370a2b14eb021f4

Observation 6fc91eaf-d742-4e7d-9e35-b4429f217f23 · outbound

This paper cites write newline.

Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition write newline

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-05T06:03:55.593835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T06:03:55.593835Z digest=sha256:e3a27534b85100c79c6075828779b82c4104bded21a1b7fbf70f1f9af310708a

Pith citing papers

No inbound Pith citation observations are available.