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Paper Citation Record · LEDGER

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

As of 11 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 2026-08-11T06:34:44.6726+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=sha256:528086d41b9e0927d0f2e132a80d0dc02b28a8f2c0facaca9e75ddd0b081c581

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
raw_fallback, observed 2026-08-05T06:04:12.684373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.078285Z digest=sha256:940435f489bfcb5d45ad49be4d8ed8c2b911deeb3f23a92cabcf7ccc4781aa03

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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verified fuzzy
raw_fallback, observed 2026-08-05T06:04:12.448374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.199802Z digest=sha256:bcf146b170b511372e88f3b3fff2ad9964f5404762ef8444dd74903e3f86275a

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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raw_fallback, observed 2026-08-05T06:04:12.267610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.375344Z digest=sha256:3789cd84b1366e740beae9a4674dbae97437b0ca86efed0c3eede14eaf6a03e1

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.514313Z digest=sha256:a1726d8ca589954d167405c7a724e25d5762da7dfc0b37e9153ef25be87b7f01

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=sha256:90d6988bd8b193159693ecd3a64b1de556ca23b2b5ef121471bc4653479f5e58

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:46.905433Z digest=sha256:a8aca0fc3894332d80bc6b06aca2dfb6ee113f9d0014d737abeb4644eca99624

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:e215eb6c8f90ce73c277a0d5eac118cbc29f84a621ee4ad047bfb1623bb0d599

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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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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:47.374782Z digest=sha256:8759678a41462d2bf6bc0e6f28c026a0837cdd919f9d9d1c725b9eda2fb5afb5

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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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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:47.604746Z digest=sha256:ebbc6490a05925aa423b0e27a7c9e5049bf158540ba846ba77eee28f189bb1b9

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:eb635557b5acf987ab9dd06ac08997a31b79cb0865a29b703c4e076e16e3e791

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:47.924799Z digest=sha256:07ac7883c2a421f5de01a412118955d017adb9ef2346fcf1eed30b60d0c5c944

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=sha256:333c889edacc60e91a7db6b9bc8919b74303b4c89019ca54be0af3810069b826

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.184768Z digest=sha256:2bbfd8a441f8c0e7ab20f1e110c9b593decf3014db1331e3c978e73dfabafcd5

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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raw_fallback, observed 2026-08-05T06:04:10.115799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.299516Z digest=sha256:3beef4e23125f6aed3e081741c906116f9addb4383cf8d360e783ed31a65ab55

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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.467221Z digest=sha256:811364625dfa6dd67cf5a8152945b59d52e72d061c57450b9e3123fc3abd82e9

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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.604740Z digest=sha256:ad1a250aedc8e92116da9a7c502301cd06a960ecb2dcec77f92ee07333c59d7a

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:48.844805Z digest=sha256:47c8807226483f6a16770e8781c35a457468c31b9818a98c350e62ba7d084546

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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raw_fallback, observed 2026-08-05T06:04:08.894748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.077030Z digest=sha256:e337cd983bd19113dbcffaf2b3e759deeb655270ad477ed7b8d9b1c3c4442e61

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.203508Z digest=sha256:da8fbee5d3c9473b4e81cdeea087d67a1722ce911bf80989f05b7f432387f8f3

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.330505Z digest=sha256:655011e01f80a89819694c5e668ebf768fc71ec463c7b636eccae76da60f8bc9

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.454835Z digest=sha256:60fc68901c813bb9b335178822f05d2cfb01e235dcbedc8e62b6075f3443f3dd

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.624740Z digest=sha256:7925f41a1240923bf3fcc8e51bae974a2a7ad09c84c52e3343ce84f4aee8298e

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.784748Z digest=sha256:10ea646af89cc9b210154e60c5f1153ed028e9d28ff4db7d5f37b14f4d64f042

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:49.904468Z digest=sha256:bd5054f0e10362ca9e96e4e687ed95a2f480ae582f546fd0b4c1d3ae100a0603

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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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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.114339Z digest=sha256:cc28199e1a92bfe00105fd728583e0c63147c051360a6217e6a105757627450e

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.247839Z digest=sha256:6a8c0fde65b5778d85b8271ab19ad052c4d8feb07f53d9ff5f6aa0c581744416

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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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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.474744Z digest=sha256:e0e0c47f71db43adf89dcb8694cbeb359d88588165db931a224222c0dafb77b0

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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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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.744742Z digest=sha256:28614bb560259b0dba6b07b8e61116ce89273709634919f7349b4ddc983426fd

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

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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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:50.904747Z digest=sha256:3e31f73287aad4b6892ee71d27cadcfc94fab946229a42f68cb5373f85cba2a1

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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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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.066096Z digest=sha256:f1309895a424336eb5cc905fafdb1995c70ffaa0e9d1e16828fdc01c4652d0a6

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.216553Z digest=sha256:00edf7cfb8ec4707f50051147a546684c01db274badcc931021468f03e013dcf

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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verified exact
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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.337405Z digest=sha256:33aa8404a291955ccdf774367e9800f63534ddba74c27bece0f591ddbc8b81b6

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.524746Z digest=sha256:ae6a5d9561e1720b75baca70a2d24c299a8d441f7a93b93d3712ce3c3ca49b2c

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.669011Z digest=sha256:fca40bc635c6ab6ed01fcfe834c027d614b16bd2eb853941cb4fecdba365a587

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.800697Z digest=sha256:6ba60762b5ea38ce478d63d7cf62f3208cf55a2b0167ea7376b845a232fae046

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:51.973057Z digest=sha256:7135e309a165c6bc44385ee5d0568119419de5c6ab9dc27c35ceacf20b7003fb

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.183004Z digest=sha256:71950cf2b76fee3a33a0553f420b0d80bb5c1303c4ef50357018f9ca959ca835

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.304745Z digest=sha256:e4c5cd0b01ee34312fffda2192007f3e4afc7ed6066a702906e10477dfb96b0c

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.419182Z digest=sha256:8fa67bb5c2a63b29115669f823e1cc37dfd3b0ba0de8928fbe3c7047a7dfd9b7

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.541340Z digest=sha256:76f7a5fd1fd8165b8158b284bec74eed09187d5ef2830e19d0a4b2d5425f743d

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.722187Z digest=sha256:ccd4fd53448501e021dbdb0a43aa1daade3f176d0556b4a5ce8931dd51fa612a

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.848428Z digest=sha256:a2054eb44071a6cd5bb137fa77a66bf95d339af5516be5b288470a91e07cf181

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:52.994741Z digest=sha256:f223ada4377c9af3394ab673d095bc1dd33077c1416d9600d8c93918ade70d70

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.222767Z digest=sha256:669ffe9345645529e8dbce5790f53ce8312d0e3cb40d47164b8f7ad48c350933

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.304862Z digest=sha256:8f51d8ab2d2c6f0dacada2e0ea08125fceb2b04a9ab90ba8f7b87af2e969e7a1

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.464879Z digest=sha256:b6da5f1a99e9164039490c1a9fa4445c7a32be4d379ea711852a1149867cd5bb

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.591328Z digest=sha256:f4d4b793c148e4c640826d5724f798ecae7b09655c358a2f24abf50a7c29cd61

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.744129Z digest=sha256:a32e7fd70c04c49da00870d9176a038b0cac26eddf2f2122b5b7ba54cde2293a

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.934738Z digest=sha256:bb60ec793b9af48c77fcf58d2a9967d46b6859146c1fa8f7547cb7a272bdab48

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:53.994740Z digest=sha256:68a6268df0a8c6906cc07fbc076397218972c91454e35792a9d031a20fca8119

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.146602Z digest=sha256:c87797ffa0a4d7cb94ab2a4e70402ca5fd997fdab15573a27ecdf2f92f934d4e

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.306833Z digest=sha256:3bfa5f907d939e29d991e2c9280bd5e1c2e61c294c4c6578b2f97f1a05bfaa17

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.422488Z digest=sha256:07c0e8c1cba33d996d61fcd8f7187501537591f76cf28b37dd01a0dd75c6a102

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.604743Z digest=sha256:d044325b343129231a7ff937f6bfd0c4bce3921bffa6bc10d845f7447ecd68b5

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:54.849479Z digest=sha256:cd7b43573ba259bc7a2c7df245ff71dfe4ed64d322864743713d404949c6685e

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:55.044880Z digest=sha256:d74549b249e0d1964cc63948b5ae5a98222508c2aceac304150134fbecc7e88b

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:55.204740Z digest=sha256:242e538a57cd91102ed8d49f9ab5b90a5c2cbae91d9f1268649035c7626f44e4

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:55.286091Z digest=sha256:2f7006e6302acdda7deaac9063e4d90dedc3bd5868efd3316828392bacb549f8

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-05T06:03:55.499423Z digest=sha256:8a4a9d904fed6fc68a22b4287f18303e11a00c6824851eb22a14775d3a30909f

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:e5905707ce8dfc4f6d618ab73ca424a482961f2bf6e22a9473a28420fec554a5

Pith citing papers

No inbound Pith citation observations are available.