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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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:30:15.743550Z digest=sha256:1fd671b945d9d1ba467592f3dd88a00ea7347679d3e6e2d74c147b0563d7ad7e

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

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=2026-08-09T21:30:15.748845Z digest=sha256:7c383ec28f0c927df3f1b12a426dc6dd41ca3b2b88cf27c985268ca3519c083f

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:30:15.752756Z digest=sha256:c45cd40f7c5da4fc37743a8a9e355508e849ae8ced2cdd105203cc3b8e7a8e73

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:bc0e4f9c188621ddbbdf0cb28d8a0061e0ba0d26eb936e5c7bf8a02a401e4bd5

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=sha256:7310a4f46e2cceac5cd1f782ce159e0ddde0f2e86ff390992bb2a889779b747f

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=sha256:702eba95e939d9a720dd5c28dc9b36491ee5665b7ddc140c518aba864bf92c73

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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raw_fallback, observed 2026-08-09T21:30:16.878557Z

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=sha256:53fd2cb61a5d1b7b083f29e91f05be1b12e77bd9834251315411bc943210d1b1

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=sha256:00fd495ef3caaf7bee2287f118fc33411aec4522276d50aafe20b4b0c2464cbf

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=sha256:1b9f819d64de383c10de177b80af80296a4032a3d7e981026f54057c69992033

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:b6c6a339a7531a029942e67dde1f6152a7f4a00152959a1414f53befb081abe0

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=sha256:2a4a40c03e71ba2adc2729f105f4b1169c710a593989c758f5af83cf804af251

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:a7ca6b0c1ed10b2f804017a7e52c871aa8ff082cbb2a647a70d1c1d3453d8140

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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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=2026-08-09T21:30:15.796553Z digest=sha256:cb5fdbc769b5e60f1e7959397943e1d5f010521dee800682bedae9074d6a82e5

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

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=2026-08-09T21:30:15.799938Z digest=sha256:8a30d277dd361cce679a1b4c1dbb0d9e6efd5c6f4073be6f278a13b19856cd43

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:30:15.803946Z digest=sha256:9579bbe968f46957435f146decca2fd8c2d159bdd130afb81aa9adecb06d3078

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=sha256:043310a7da3619e20bc013ffc5e7e00da6dc77fb7629ecc944bdc2d4a2e206c1

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=sha256:83bf2dd1434d98c8f44998df3af03c2c3c201be15d7b191d8fd75d63f5b85f11

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=sha256:815bf80beec9e79e5360c489d6250dc3d14cbf231129fae2cff3fd17c962194a

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=sha256:1efe036e3145cd47abc171bdf75b7e386f80611c22912a32109406baf077a70f

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.

source=arxiv_source observed=2026-08-09T21:30:15.821038Z digest=sha256:2661cc2cab36082e09f2d513eef53900494f2234e6e68fe16180d015bac8e19f

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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raw_fallback, observed 2026-08-09T21:30:16.816392Z

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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=2026-08-09T21:30:15.824363Z digest=sha256:20f9aa75f7859daeba2b01ed1d194e630968f5f138819daa62f63efd5d8f8be4

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.

source=arxiv_source observed=2026-08-09T21:30:15.827707Z digest=sha256:aa354a07b41510f425ce9d394e799de2f60dc003a9dc19a5d817622c698221d8

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

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=2026-08-09T21:30:15.831110Z digest=sha256:cfce906bbf73690a08952b6a25bb8b68534ea47cfd2564cee8ce525973916b31

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:e3f156c50c74f8a7b8cf6bf04e5a8442b896a3570647a9af977904c8a790d559

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:a357f2dfa542e327628f40766117ded2ef9496074e4a8d32fd482c596de9d68d

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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raw_fallback, observed 2026-08-09T21:30:16.785031Z

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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=sha256:2c1137aec11eb7a21582345863c3631a27c3ec1e8b571eadd3c6dba02c177cd0

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=sha256:80ebfabaff5e8267d607641baa4306a00eac4b75c896155f8b9d228315a79b67

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=sha256:970c02bafbc8ac086f11104cc3181d2653ccd65a7b0ee62f3c7f8b532c928899

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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raw_fallback, observed 2026-08-09T21:30:16.752890Z

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=2026-08-09T21:30:15.853467Z digest=sha256:b020563565a8d5204441b83b30da849699c0a6e90c632c6093666970cff05de5

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=sha256:5dc21bdf8533ab6b72323238378c8a454e3c3d56c814c3a64b1fa9ca999bfcf4

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:d7965ffc78f5c52944e8cdfed2865a2cfc2dcc2918b2dad2ac1151bac12dbe0e

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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raw_fallback, observed 2026-08-09T21:30:16.728692Z

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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:dce243d6e7658ed06d7bff7ed57f03ccee0581df8490395a04652ff45ed91080

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:de20279ccd5d4dc4d7dee31524ec19910593bdce337310287bb3c4e8ea9f8599

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:b6ecb8aeb4917db1211e2793c38f191b00ade87f7b1f2b6907674f79388b404c

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=sha256:51ce5ac7673d582ba40ea234edb54c42d342a1c2c144cdf7787f017500cc5860

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=sha256:0d09f3bb2a10ae05e9476a75e8179cc9aff03c67b7f55a87f7fb8b4564e038f2

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

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=2026-08-09T21:30:15.882631Z digest=sha256:180d67b6552450cf909aa04cea26b7b13ea873b731b5209aa91862535a2f0519

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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no resolver link, observed 2026-08-09T21:30:15.886181Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-09T21:30:15.886181Z digest=sha256:79c97ea5957406d8604a5a720824bec8608d6765213dc05d1165c71223540a76

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=sha256:9c0f7cec50426ea520653ab43281980b05061a7c5ec9246cb9cb5842485f8c05

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=sha256:6176ee63c6ff1ed119bb2561d1829b024719f7e8a043564b426b7fcc824a6943