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Paper Citation Record · LEDGER

Differentially Private Policy Gradient

As of 10 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2501.19080.

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

pith.paper-citation-record.v1
2501.19080 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:30:15.890050Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T04:43:58.646419Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T04:45:54.928527Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact4
  • verified fuzzy15
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4e579b9e-a72b-46ed-9d9d-dee39411f9d3 · outbound

This paper cites write newline.

Differentially Private Policy Gradient write newline

Reference 1

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:30:15.743550Z digest=sha256:8351e155fa5ae246eeb24dd46854d75440019d631a2091feaf5c13ef409139ba

Observation 4bed5896-1892-4950-adca-b5592ace892d · outbound

This paper cites J., McMahan, H.

Differentially Private Policy Gradient J., McMahan, H

Reference 2

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arxiv_id_nonexistent, observed 2026-08-09T21:30:16.681801Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.748845Z digest=sha256:0dfc41550cfd00416bbbc5d9681d4a6d97f86273de5e5534423097a9b5e6d4e4

Observation 7360e828-5dac-4d69-bcf8-0be359b1cacf · outbound

This paper cites M., Crump, T., and Far, B.

Differentially Private Policy Gradient M., Crump, T., and Far, B

Reference 3

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source=arxiv_source observed=2026-08-09T21:30:15.752756Z digest=sha256:c59056bec0afa14866c6bb149055a477aedb09233a777fa4c4b18fcfaf25d295

Observation c55c6199-3017-4a66-9793-241b1fb055d4 · outbound

This paper cites OpenAI Gym.

Differentially Private Policy Gradient OpenAI Gym

Reference 4

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source=arxiv_source observed=2026-08-09T21:30:15.757092Z digest=sha256:d3cd2e1b14eceac5a08dbdef3d02b3dd481b7ed877ac670bf71769c66cc58817

Observation 3b603473-cbb7-45ff-bd59-5629d312af3a · outbound

This paper cites B., Song, D., Erlingsson, \' U ., Oprea, A., and Raffel, C.

Differentially Private Policy Gradient B., Song, D., Erlingsson, \' U ., Oprea, A., and Raffel, C

Reference 5

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raw_fallback, observed 2026-08-09T21:30:16.890038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.761943Z digest=sha256:4b66f72ddcb37840c7374bed34025ee77cc6713b33cddeb39a6cdcdbf4a09488

Observation 4796ad30-7028-4293-bd69-5173272fa7fa · outbound

This paper cites Differentially Private Regret Minimization in Episodic Markov Decision Processes.

Differentially Private Policy Gradient Differentially Private Regret Minimization in Episodic Markov Decision Processes

Reference 6

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source=arxiv_source observed=2026-08-09T21:30:15.766166Z digest=sha256:41025e38079d7861aacb417e749a60ffa24319f08caffcaeed9fc0cabc4e188a

Observation 9856e454-0b9f-4e06-a90a-d093618f274e · outbound

This paper cites Privacy-constrained policies via mutual information regularized policy gradients.

Differentially Private Policy Gradient Privacy-constrained policies via mutual information regularized policy gradients

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.770724Z digest=sha256:197f9df223e3a27e780d4e4ed5ad2f5410dd0ba8f9930c017d37114561703722

Observation 61320afe-e3f7-4a8c-bc25-4e84a68029ed · outbound

This paper cites Methods to integrate multinormals and compute classification measures.

Differentially Private Policy Gradient Methods to integrate multinormals and compute classification measures

Reference 8

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source=arxiv_source observed=2026-08-09T21:30:15.775407Z digest=sha256:94d4a818446cc9541e257a3b2206235d519e6780e228ae236b3e24541b918630

Observation e378d8d6-1ce5-4d60-bfe1-29842c7f2e1d · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Differentially Private Policy Gradient DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

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source=arxiv_source observed=2026-08-09T21:30:15.780088Z digest=sha256:67ad54ce2e618b82fef006f7d1fd7ce4ba902ec03ac3260e45f3590187ebb8fd

Observation ccc0626b-abbb-415b-8c42-27d374830c30 · outbound

This paper cites Differential Privacy.

Differentially Private Policy Gradient Differential Privacy

Reference 10

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raw_fallback, observed 2026-08-09T21:30:16.867951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.784240Z digest=sha256:ab58cf4397ca4234b4053357bdeb90d1bb75e8d9ed565a1e1973de9f6511631c

Observation e8bf427f-d0c0-4209-b46e-6a5a0e81cf30 · outbound

This paper cites Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration.

Differentially Private Policy Gradient Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration

Reference 11

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source=arxiv_source observed=2026-08-09T21:30:15.788544Z digest=sha256:394b533ed2289fa41c2ecf4204f9db9f0c79b34513125d6f58cfd0411b6de527

Observation 54a366c5-8195-4ec2-b3d0-bb8a3daa6366 · outbound

This paper cites Membership inference attacks against temporally correlated data in deep reinforcement learning.

Differentially Private Policy Gradient Membership inference attacks against temporally correlated data in deep reinforcement learning

Reference 12

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arxiv_id_nonexistent, observed 2026-08-09T21:30:16.422345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.792957Z digest=sha256:7f1d07c334f35e067c86ec09fe80418f6a714e35b2855511753a7aa15c249902

Observation 9eb40fc7-c78a-4c9f-adb2-c55053d1b21f · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Differentially Private Policy Gradient Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 13

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raw_fallback, observed 2026-08-09T21:30:16.857332Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.796553Z digest=sha256:a217237c948422968a13fa5f88227f03ad22689bcc5fb27baec4b1f37c7db997

Observation 98aa3d4b-bbbc-4a58-a10c-8f389b5edfcf · outbound

This paper cites Recent Advances in Reinforcement Learning in Finance.

Differentially Private Policy Gradient Recent Advances in Reinforcement Learning in Finance

Reference 14

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local_arxiv, observed 2026-08-09T21:30:16.187990Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.799938Z digest=sha256:247ca1290ad21a175b69308ec9d3bc13e957d864337bbb85593904ccbc4025cd

Observation 8bb07358-54e9-436d-a945-5e87c39b0636 · outbound

This paper cites an unresolved cited work.

Differentially Private Policy Gradient Unresolved cited work

Reference 15

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source=arxiv_source observed=2026-08-09T21:30:15.803946Z digest=sha256:cd54c505c13323b1d692f63cdcb0e17675db68dbb518df90227fdae696cc358b

Observation 9d991cd4-db4f-4751-a8a7-986c7acff841 · outbound

This paper cites an unresolved cited work.

Differentially Private Policy Gradient Unresolved cited work

Reference 16

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.807414Z digest=sha256:723e4081e617b2984a0e9cac874ba042da45e4ea2689a301baa388353e0cf2d0

Observation a29345e9-708a-425f-8324-80dc44080024 · outbound

This paper cites A survey of reinforcement learning from human feedback.

Differentially Private Policy Gradient A survey of reinforcement learning from human feedback

Reference 17

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source=arxiv_source observed=2026-08-09T21:30:15.810672Z digest=sha256:47ecea14595d30d87beba10d0c2499793b6d0ef3ebf1c9fb7bbbf587abe8804c

Observation f5806525-bfd6-4f82-9523-34040fabf55c · outbound

This paper cites Continuous control with deep reinforcement learning.

Differentially Private Policy Gradient Continuous control with deep reinforcement learning

Reference 18

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source=arxiv_source observed=2026-08-09T21:30:15.814030Z digest=sha256:233196cfaf296120c0e1223c36fa9a9abc6155df74704907849493eca3d9ef42

Observation 5e2b564a-64c2-47d6-b4ba-aa5ae426eea1 · outbound

This paper cites Deep reinforcement learning for personalized treatment recommendation.

Differentially Private Policy Gradient Deep reinforcement learning for personalized treatment recommendation

Reference 19

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raw_fallback, observed 2026-08-09T21:30:16.836423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.817552Z digest=sha256:481b0297612ac28c09bf5700af524c1c1f40eb7d6275005c44d3cd634b046cba

Observation 86f356fc-1302-4c13-8deb-52fe89c5b104 · outbound

This paper cites B., Ramage, D., Talwar, K., and Zhang, L.

Differentially Private Policy Gradient B., Ramage, D., Talwar, K., and Zhang, L

Reference 20

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raw_fallback, observed 2026-08-09T21:30:16.825984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation fc6cffc4-05ba-4d9c-b687-bdf7b9e5087b · outbound

This paper cites P., Mirza, M., Graves, A., Lillicrap, T.

Differentially Private Policy Gradient P., Mirza, M., Graves, A., Lillicrap, T

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 9c915feb-6a6c-4499-af10-d5719ec452b6 · outbound

This paper cites an unresolved cited work.

Differentially Private Policy Gradient Unresolved cited work

Reference 22

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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Observation 1ec8fa84-a5fe-412e-bef7-2adf4bc6b005 · outbound

This paper cites How You Act Tells a Lot : Privacy - Leaking Attack on Deep Reinforcement Learning.

Differentially Private Policy Gradient How You Act Tells a Lot : Privacy - Leaking Attack on Deep Reinforcement Learning

Reference 23

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raw_fallback, observed 2026-08-09T21:30:16.795169Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.831110Z digest=sha256:dd68f8fcd8c5ecdaf54ae8e65f0a2a5dd3e4276ec888d63a4020fd5d0e0e240a

Observation 16aa96e0-eee4-4233-8248-3e3ac9280947 · outbound

This paper cites an unresolved cited work.

Differentially Private Policy Gradient Unresolved cited work

Reference 24

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doi, observed 2026-08-09T21:30:15.951861Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.834687Z digest=sha256:7beef85dd1dbc190b3aa01a2b0a0c715fc4fe89522b477896e6a76adf675243d

Observation 2e1bfbff-43c0-4b2f-9bfd-9a06b4a60566 · outbound

This paper cites How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy.

Differentially Private Policy Gradient How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy

Reference 25

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source=arxiv_source observed=2026-08-09T21:30:15.838186Z digest=sha256:c887b3cd1de8709b53e47ef817580d75cd1976dac95876e4f42911e59f33b84d

Observation 6017f280-3641-4e94-907c-7589e015429c · outbound

This paper cites How Private Is Your RL Policy ? An Inverse RL Based Analysis Framework.

Differentially Private Policy Gradient How Private Is Your RL Policy ? An Inverse RL Based Analysis Framework

Reference 26

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.841902Z digest=sha256:ecf00b5d0a367df9b8ce53e20f331e59592800ddce7ad16740b8e499b1920316

Observation e30f0d98-ab8b-415e-8429-642cc5cb1f48 · outbound

This paper cites and Wang, Y.

Differentially Private Policy Gradient and Wang, Y

Reference 27

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raw_fallback, observed 2026-08-09T21:30:16.774343Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.845374Z digest=sha256:daac9a95d1fa159e7a7e18cbf3ff2ece5e9f4cfd705efa22ef85ad575afa0496

Observation f40f9856-df6a-4fc4-b096-3ada96cd235f · outbound

This paper cites Reinforcement learning for personalized medication dosing, 2019.

Differentially Private Policy Gradient Reinforcement learning for personalized medication dosing, 2019

Reference 28

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.849825Z digest=sha256:0a0b3c757b1ede177e9bf5c1e2adce2559051f4356e22872b194d9209a49c1ff

Observation 4cdd5c8e-4306-4e8a-86ea-a931f2194d69 · outbound

This paper cites I., and Moritz, P.

Differentially Private Policy Gradient I., and Moritz, P

Reference 29

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.853467Z digest=sha256:609d1b761445864ddac772c069c2b38814b7a1ce3529db6158640aba6e0cdce9

Observation 58a94455-3690-4281-9df3-36c19eb107e1 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Differentially Private Policy Gradient Proximal Policy Optimization Algorithms

Reference 30

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source=arxiv_source observed=2026-08-09T21:30:15.856751Z digest=sha256:95731b4a7ef5c63118424026a19e3c67a015950f33093e702b976e6030f5d016

Observation e9ea94c5-e311-47c9-befe-9f8df2f49c1d · outbound

This paper cites an unresolved cited work.

Differentially Private Policy Gradient Unresolved cited work

Reference 31

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.860829Z digest=sha256:e1caa279a3ed4e2b3273650c678a257eaf6185f9a5b2f21bf58b04f6a0aff7f8

Observation bc2fa47c-523b-4442-8f80-2a4fbeb1b744 · outbound

This paper cites S., McAllester, D.

Differentially Private Policy Gradient S., McAllester, D

Reference 32

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.864645Z digest=sha256:bc573295ff7a1c9d0f9c7b3cb74339f025bb391bf459750e048905082ba0c594

Observation 22933b28-81d1-46b0-9dda-b09224f8b04d · outbound

This paper cites Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes.

Differentially Private Policy Gradient Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 33

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source=arxiv_source observed=2026-08-09T21:30:15.868108Z digest=sha256:45bcb6d91c3ae8ebd798b79dd4f32f15cdf5f9140b63b0bcc9185a8c806424d7

Observation 37582d23-0789-4d06-866d-3238b0a7e8c8 · outbound

This paper cites Mujoco: A physics engine for model-based control.

Differentially Private Policy Gradient Mujoco: A physics engine for model-based control

Reference 34

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raw_fallback, observed 2026-08-09T21:30:16.716513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.872020Z digest=sha256:8e0c9561b308fab5c43d9d5df238dd3447d19a7253e64efd38be550f81ced359

Observation aeec1a33-2088-49cf-bc48-e0b0f1a7d00d · outbound

This paper cites an unresolved cited work.

Differentially Private Policy Gradient Unresolved cited work

Reference 35

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.875687Z digest=sha256:799d1f4c8df986bb7974fce71921ee66146a5377ad34cb5458de2cd95575f827

Observation 8c03bd35-273c-47b0-874b-f0d7ff36361e · outbound

This paper cites and Hegde, N.

Differentially Private Policy Gradient and Hegde, N

Reference 36

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raw_fallback, observed 2026-08-09T21:30:16.694584Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.879280Z digest=sha256:bc80bcd6825cbe4ac4caa1ef7dc6a3c286dfc97e37e6031f598f716c09cce176

Observation 17b81c7b-481c-42d4-b074-7d85b34171d4 · outbound

This paper cites an unresolved cited work.

Differentially Private Policy Gradient Unresolved cited work

Reference 37

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doi, observed 2026-08-09T21:30:15.929584Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-09T21:30:15.882631Z digest=sha256:2011aa83af707f8decd37216ae37d8c6ddb29c7297ea70d662c9643ac17359ab

Observation 74eb0231-9a03-4bc4-ba59-572920e52a16 · outbound

This paper cites an unresolved cited work.

Differentially Private Policy Gradient Unresolved cited work

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:30:15.886181Z digest=sha256:d3c9702dc5ab711afee06c289418f284ec059c73d262ab5254e2039af80a76a1

Observation 3f8eff11-ecdc-4938-999a-3c96d89310aa · outbound

This paper cites Reviewing and Improving the Gaussian Mechanism for Differential Privacy.

Differentially Private Policy Gradient Reviewing and Improving the Gaussian Mechanism for Differential Privacy

Reference 39

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no resolver link, observed 2026-08-09T21:30:15.890050Z

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source=arxiv_source observed=2026-08-09T21:30:15.890050Z digest=sha256:8493746d655458f66ec929103288cbf53a63c385dca5f43f3ac644652f49cdc0

Pith citing papers

Observation add2b129-436b-490f-b027-c5b947577afd · inbound

On the Sample Complexity of Differentially Private Policy Optimization cites this paper.

On the Sample Complexity of Differentially Private Policy Optimization Differentially Private Policy Gradient

Reference 12

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arxiv_id, observed 2026-05-18T04:45:54.931081Z

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source=pdf_text observed=2026-05-18T04:43:58.646419Z digest=sha256:831248184e274abcfc25a27efd538095778b0e6976865a378d366643b943ff16