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Paper Citation Record · LEDGER

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning

As of 9 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2505.20621.

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

pith.paper-citation-record.v1
2505.20621 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:58:13.783532Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

65 of 65 outbound references displayed

  • verified exact7
  • verified fuzzy28
  • unresolved28
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fff5ac11-2e8f-464c-8dbe-29197c94ade4 · outbound

This paper cites write newline.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning write newline

Reference 1

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unresolved
no resolver link, observed 2026-08-07T13:58:10.345268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:10.345268Z digest=sha256:164f31cf5518888a36dbbe435868185822a6c13c1fdfe107ea1cec120770b313

Observation e47827d9-1d46-4011-89c5-ad04c65b6f87 · outbound

This paper cites Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang

Reference 2

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no resolver link, observed 2026-08-07T13:58:10.395338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:10.395338Z digest=sha256:cb8d9dd9d453e942e39ba671951ef1cc9f2e5f04d0fae0b89eec6c75a6cc3700

Observation eb48bc7a-e6ce-444c-abb2-027743bdec62 · outbound

This paper cites Differentially private policy evaluation.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Differentially private policy evaluation

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:20.063936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.453612Z digest=sha256:03f7f1a449b22e1915786246ceaf977b3389479981ceae89701d570eb9ce534b

Observation 394eb3f3-fa2e-4228-8a7e-37cd469125b4 · outbound

This paper cites Hypothesis Testing Interpretations and Renyi Differential Privacy.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Hypothesis Testing Interpretations and Renyi Differential Privacy

Reference 4

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verified exact
local_arxiv, observed 2026-08-07T13:58:15.945700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.510887Z digest=sha256:8d3fa9cc7f59abebbe35eac51ca6fd1cbc3ccefa7886d966ed36f3aa8fb33e72

Observation 6658e4c2-1756-46ce-b56d-1a7ccccedc6b · outbound

This paper cites Defense Against Reward Poisoning Attacks in Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Defense Against Reward Poisoning Attacks in Reinforcement Learning

Reference 5

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verified exact
local_arxiv, observed 2026-08-07T13:58:15.654771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.575392Z digest=sha256:e456bfaec428e789463b286028fa4a8db9fd13cc9d3a43c31b3034251e1a9ab0

Observation 2a541fa7-5d70-4c73-a5e8-ad70f3df3408 · outbound

This paper cites Can M achine L earning be S ecure? In Proceedings of the 2006 ACM S ymposium on I nformation, C omputer and C ommunications S ecurity , pp.\ 16--25, 2006.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Can M achine L earning be S ecure? In Proceedings of the 2006 ACM S ymposium on I nformation, C omputer and C ommunications S ecurity , pp.\ 16--25, 2006

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:19.889656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.618171Z digest=sha256:f6bfaf0490d7448cd122cb04d5e1d74ba8711abd8b4e59e3cb1954c3282b85ef

Observation 1820ff58-aef0-407f-805b-3014c478a83b · outbound

This paper cites A Distributional Perspective on Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning A Distributional Perspective on Reinforcement Learning

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:19.766424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.665286Z digest=sha256:df0dfcdf341ffd3553a7e823272d91c406fc452af878f0a2987650dc3ed89280

Observation 3ad9621a-4b7d-4fb2-af97-580d816a663d · outbound

This paper cites Poisoning Attacks against Support Vector Machines.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Poisoning Attacks against Support Vector Machines

Reference 8

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unresolved
no resolver link, observed 2026-08-07T13:58:10.743477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:10.743477Z digest=sha256:e35b70fd69d7891af14678927ef3b412b462d2647d922bdfe92b9c8dac2e570d

Observation 9c3c8726-1fb6-4669-9bca-dcf89c27e2a3 · outbound

This paper cites Double Bubble, Toil and Trouble: Enhancing Certified Robustness Through Transitivity.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Double Bubble, Toil and Trouble: Enhancing Certified Robustness Through Transitivity

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:19.575732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.791538Z digest=sha256:e30a3a298dd95e7453d6c0402e9227bab1b1998e34becc6c02ff684119e518be

Observation 0c7e1589-336b-418f-836d-fa45d714bccd · outbound

This paper cites Cullen, Paul Montague, Shijie Liu, Sarah M.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Cullen, Paul Montague, Shijie Liu, Sarah M

Reference 10

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unresolved
no resolver link, observed 2026-08-07T13:58:10.835804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:10.835804Z digest=sha256:2197799dd89e32acb4044b44cf4e9cc9f08da415f20d88a076dd1872e6224ba4

Observation c827b364-f57e-4602-a48b-4b228d703595 · outbound

This paper cites Et T u C ertifications: R obustness C ertificates Y ield B etter A dversarial E xamples.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Et T u C ertifications: R obustness C ertificates Y ield B etter A dversarial E xamples

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:19.431300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.893900Z digest=sha256:3b4ba1d6cf6f508bc6dac1c4e063830ff95ba4839da25421d4154fa4055f833d

Observation a143930a-313a-46dc-890b-aa8017a12b1c · outbound

This paper cites Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:19.260158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.946046Z digest=sha256:d7d4264aba56ca2bff20fd7cd7f7967004ed155d7a82360fc1b12cc7cf2a0df4

Observation 9a0c3942-9daf-4d22-97b5-d6abf63b58c8 · outbound

This paper cites Robust Estimators in High Dimensions without the Computational Intractability.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Robust Estimators in High Dimensions without the Computational Intractability

Reference 13

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verified exact
local_arxiv, observed 2026-08-07T13:58:15.329140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:10.990606Z digest=sha256:f0f52825ecd713829f3763966b1d9fa63aa8a66d68c1154bc1e03290677c651a

Observation 1dd4274f-52da-4f4e-8360-10c4006ac579 · outbound

This paper cites Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:18.990864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.044135Z digest=sha256:35b676065a7964b448c41b30badb57f7832025a59ebd638effb65ff37056ff19

Observation e049882e-5244-45c9-948a-2d17167dc24d · outbound

This paper cites Calibrating Noise to Sensitivity in Private Data Analysis.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Calibrating Noise to Sensitivity in Private Data Analysis

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:18.780176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.103866Z digest=sha256:8c960fdfb7dd501ebaca5e7d0c7990b3958c0da967a21f8afce01ff48f181c89

Observation 34ad001b-1bc7-4cd4-a55d-6d742f0b7403 · outbound

This paper cites Data Mining with Differential Privacy.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Data Mining with Differential Privacy

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:18.582555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.162546Z digest=sha256:0e9949ef3f074319ca930bf13f6df24ce1388f6c4fc221abfcf4b395ac57bad3

Observation 3f26c35f-b011-4774-b079-67ebe023fc42 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning , 2020.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning D4RL: Datasets for Deep Data-Driven Reinforcement Learning , 2020

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:18.447912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.237113Z digest=sha256:661f30cbca5457fd4ab82f1f52ab544bf2370ec74a09491bfa5d32904b8dfb36

Observation 67aa7d41-d589-405e-abf6-56ef7c52c8af · outbound

This paper cites Sugli integrali multipli.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Sugli integrali multipli

Reference 18

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verified exact
doi, observed 2026-08-07T13:58:13.921093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.288564Z digest=sha256:81f6825708e55ddf863930bcb16630249400dfbe4aa84157de2b211cbef1fdbf

Observation ccd6209c-2cd5-4d85-8e34-4ad93078cd26 · outbound

This paper cites Local Differential Privacy for Regret Minimization in Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Local Differential Privacy for Regret Minimization in Reinforcement Learning

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:18.286662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.334231Z digest=sha256:64c04c5f3ee1b2315cef1473c6a02ff01d4b23c71763e0ec1c5d16a2dda30292

Observation 789f2d58-fd88-4457-8a2a-b55a68f3fa53 · outbound

This paper cites The optimal noise-adding mechanism in differential privacy.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning The optimal noise-adding mechanism in differential privacy

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:18.188645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.378442Z digest=sha256:99373fd58f2811d7677137b0ff43f60f0650c9ebef5151ddfc39edb213fffe33

Observation dc46ffc6-487f-46c0-b2d7-a1744d005eae · outbound

This paper cites Baffle: Hiding Backdoors in Offline Reinforcement Learning Datasets.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Baffle: Hiding Backdoors in Offline Reinforcement Learning Datasets

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-07T13:58:18.019283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.431883Z digest=sha256:9a9a6c611551df281668c92ca839243c3b7a19c97c756647fe19ea87974ee669

Observation 589a5bf1-52e0-4d17-83c5-2025d025f924 · outbound

This paper cites Numerical composition of differential privacy.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Numerical composition of differential privacy

Reference 22

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no resolver link, observed 2026-08-07T13:58:11.489514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:11.489514Z digest=sha256:83a74bbe72eaaef1cec8e45d802f4d23447980dfffef2b574aa86c83ea75d283

Observation 4720fc79-0604-4f11-8fca-7d0a01f03ca0 · outbound

This paper cites A Review of Safe Reinforcement Learning: Methods, Theory and Applications.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning A Review of Safe Reinforcement Learning: Methods, Theory and Applications

Reference 23

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unresolved
no resolver link, observed 2026-08-07T13:58:11.528104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:11.528104Z digest=sha256:b57fe9d2cb683ec2ca5d8b26eda007eb14892a599d33ee28dfd6b3c47bc4c0af

Observation 70277514-d98b-49ca-bf4b-bdab7c0f8635 · outbound

This paper cites Benchmarking Offline Reinforcement Learning on Real-Robot Hardware.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Benchmarking Offline Reinforcement Learning on Real-Robot Hardware

Reference 24

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unresolved
no resolver link, observed 2026-08-07T13:58:11.564713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:11.564713Z digest=sha256:48f9d001d8428d4a644c6897f076fc17c7b5f7eb282321e108b2eda9da1ade72

Observation 8881ad37-41d7-4bf2-9069-efc3d7cce518 · outbound

This paper cites Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks

Reference 25

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unresolved
no resolver link, observed 2026-08-07T13:58:11.616583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:11.616583Z digest=sha256:16e55761d5c8ec0593c4fcba525c749df5aff3788c2522fe211b2a5e35ae0ec0

Observation b2213f9f-41f4-47f0-9dba-816f334d1396 · outbound

This paper cites Learning to Drive in a Day.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Learning to Drive in a Day

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.900856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.692610Z digest=sha256:919532e9d92b36c48b1e079fefebe685975be30acb8cacd8f0d544c54a069ed2

Observation b1c3d714-7f65-4201-b68d-a5d010f537ed · outbound

This paper cites TrojDRL : Evaluation of Backdoor Attacks on Deep Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning TrojDRL : Evaluation of Backdoor Attacks on Deep Reinforcement Learning

Reference 27

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unresolved
no resolver link, observed 2026-08-07T13:58:11.741659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:11.741659Z digest=sha256:a7205aeca2c551b3c66b22f5ec1d3fc143e6bc23bcf8e78819688c226e9d2505

Observation 2bad0680-e924-4e5e-a51a-14a76bd0b187 · outbound

This paper cites Offline Reinforcement Learning with Implicit Q-Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Offline Reinforcement Learning with Implicit Q-Learning

Reference 28

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unresolved
no resolver link, observed 2026-08-07T13:58:11.789761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:11.789761Z digest=sha256:c98a99791fdeb5281b1a30aeab4b7e03ac1e87850fab21cfd529b2e949a42d9b

Observation 8cafc824-7fae-4bbc-adcf-90d19db13da3 · outbound

This paper cites Adversarial Machine Learning-Industry Perspectives.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Adversarial Machine Learning-Industry Perspectives

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.838112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.854978Z digest=sha256:bacaac2210cd9adef7d19c5b3f5297b4f38fb63473df652bf8c7f1433d345bfc

Observation a11dddfb-1c1f-4798-affe-1d6c6c6831ef · outbound

This paper cites Batch Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Batch Reinforcement Learning

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.735382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:11.919009Z digest=sha256:4244089fe8cce4f873ff549ee8a426d0a7118b20d588c82d518b9f816a75f1f4

Observation 1ab3b5d8-01d8-485a-bafc-cc90c18c0fd6 · outbound

This paper cites Certified Robustness to Adversarial Examples with Differential Privacy.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Certified Robustness to Adversarial Examples with Differential Privacy

Reference 31

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unresolved
no resolver link, observed 2026-08-07T13:58:11.963813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:11.963813Z digest=sha256:fad118f766bf87761713afbf4483b2df1670f7bc5f5e66f38df26cbd18d1baf9

Observation 71ea4002-2d91-4894-99bd-3443b068e4c5 · outbound

This paper cites Deep Partition Aggregation: Provable Defense against General Poisoning Attacks.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Deep Partition Aggregation: Provable Defense against General Poisoning Attacks

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.013805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.013805Z digest=sha256:871a55082ebb1da5238b8639a3ff8eb848d5c81923f48d619d47a7c3962333a8

Observation e1242a9c-d548-4aec-b590-875911a1c024 · outbound

This paper cites Enhancing Certified Robustness via Smoothed Weighted Ensembling.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Enhancing Certified Robustness via Smoothed Weighted Ensembling

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:58:15.003992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.074274Z digest=sha256:a04fc2f5c796d8dce305d3c8f53dce0833db32dde214d7004d9c515c02967acc

Observation ca798ca0-bc46-4a9d-a7f8-893d0f7a1bcb · outbound

This paper cites Enhancing the Antidote: Improved Pointwise Certifications Against Poisoning Attacks.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Enhancing the Antidote: Improved Pointwise Certifications Against Poisoning Attacks

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.577920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.125904Z digest=sha256:01a40ac84d34a36531eea4cece4fce8b564c778251e464318ed5ce4c4d5e462c

Observation 7a86f304-8b4b-49c8-b2ff-0ba0007b5722 · outbound

This paper cites Corruption-robust exploration in episodic reinforcement learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Corruption-robust exploration in episodic reinforcement learning

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:58:14.820244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.183239Z digest=sha256:971a6ffc631f0ec43f25c402c64bcb8178634bb5d38eb02ab59940cf3209a909

Observation 8e390ef4-365d-4800-a9f7-a5a8097c92b9 · outbound

This paper cites Data Poisoning against Differentially-Private Learners: Attacks and Defenses.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Data Poisoning against Differentially-Private Learners: Attacks and Defenses

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:58:14.661390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.229699Z digest=sha256:40dfad65ef77d62e9fda8a441e9f3e42e9dea6b078b208e0909689a90c0a094e

Observation 14e93ff9-c68b-465f-8d43-9228d5a2e3ac · outbound

This paper cites Learning Differentially Private Recurrent Language Models.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Learning Differentially Private Recurrent Language Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.290803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.290803Z digest=sha256:59c201402f8b91f34857ef4e56f2f1f84742d149cb82e346ffb57fac6bf3ae4f

Observation cc4baff2-2a95-4132-bf73-5079c709cd48 · outbound

This paper cites Renyi Differential Privacy.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Renyi Differential Privacy

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.360881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.360881Z digest=sha256:661d7efaeee0757cffb5fd2b11e26848515d3376c759fea6f055509b2a4f154c

Observation 49f452c2-1770-4c3d-9fdf-62e0c5b6a715 · outbound

This paper cites R\'enyi Differential Privacy of the Sampled Gaussian Mechanism.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.413237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.413237Z digest=sha256:2f7d6a0b7721ae61f439ca1e93753a372815c2afd266aedd422547301560deea

Observation ba73ae61-cfb2-491f-9418-3d1843619463 · outbound

This paper cites Asynchronous Methods for Deep Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Asynchronous Methods for Deep Reinforcement Learning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.489760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.489760Z digest=sha256:97c5b5c52051d91d691b7e79db3ba8f8553491acaed06d8e753b9053b4ba5d43

Observation b9684f96-d061-41b7-9a02-b77d68a8a7e0 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.548539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.548539Z digest=sha256:de314e98fd013be7e5b16033198aedcb6b397ac9c2c4abff6657dc7343fa7f4f

Observation 2a3a14f8-4fd5-463b-acde-0c206d0061a6 · outbound

This paper cites Reinforcement Learning for Optimized Trade Execution.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Reinforcement Learning for Optimized Trade Execution

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.486822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.599802Z digest=sha256:f1fc2b20fef7ec89de09e76fe14e814511ed05d0127d33c46945a39e5981b783

Observation 6f60739e-802a-4b36-a12f-9909e98866bf · outbound

This paper cites Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.413005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.656421Z digest=sha256:9c3ce0212d060630b0014c878a0ed222486e511f640b2ceb432cb243e3ef4b21

Observation d13364f2-5c18-48ea-bbd0-4e2bcb6bf3d3 · outbound

This paper cites Agile Autonomous Driving using End-to-End Deep Imitation Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Agile Autonomous Driving using End-to-End Deep Imitation Learning

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.769027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.769027Z digest=sha256:a6d8a95ad083c3e594b7e402a45e6af5bb2d268a90eff87c0221a1983b6f0b48

Observation 7acc4916-4512-45f5-b433-824142c5c1be · outbound

This paper cites Deep K-NN Defense Against Clean-label Data Poisoning Attacks.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Deep K-NN Defense Against Clean-label Data Poisoning Attacks

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.318537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.821597Z digest=sha256:6093e6d4e82e3dbccd477304cd8db819bb5635aaf6b068b6a4966356fcb9b8e0

Observation b3bd390b-7a42-4411-abd6-c24270de1d24 · outbound

This paper cites an unresolved cited work.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Unresolved cited work

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.868504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.868504Z digest=sha256:0a1f2c81032c922ea3d3ee8b2c64c27aa7f16057a4d12a04e615cf1d658f709d

Observation f7d01c77-2c05-4c21-ad04-49aa98957907 · outbound

This paper cites Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.225777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:12.915034Z digest=sha256:6108d7bcd39b5e821e4a4a3c8d3daf2cbfb9b80050abd1300f99bd571a9a4d07

Observation 6a535aef-ea08-4117-8a03-ebde7c499942 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:12.951446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:12.951446Z digest=sha256:c87285e93374e348061e3f6c623ab2ff347a67a2ddf64b9a2dac92888ab97745

Observation c5df22cb-82f0-483c-b1ab-09c59223baee · outbound

This paper cites Mastering the Game of Go Without Human Knowledge.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Mastering the Game of Go Without Human Knowledge

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:17.124406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.003732Z digest=sha256:4c37e72891c34c9dd0a54eadcaec1ad802a4bb9f3e07c96fea17eed1306ecc9d

Observation 08e662b6-0749-48a2-b188-3ec8958339b7 · outbound

This paper cites Policy Gradient Methods for Reinforcement Learning with Function Approximation.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Policy Gradient Methods for Reinforcement Learning with Function Approximation

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:16.970742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.043533Z digest=sha256:ae3a778816a93a5cd19aef2fa4342c0108e85c00cd6cd586706fd316d873aafc

Observation 7f9b2d7a-d2e7-4daf-9166-b8c4360d77b7 · outbound

This paper cites Terry, Ariel Kwiatkowski, John U.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Terry, Ariel Kwiatkowski, John U

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:13.094075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:13.094075Z digest=sha256:3ae8c41ab59377d75e07d2a9636bf3376b1b3cf1788a1940acbe367b13a205ba

Observation a9eb8d4e-018f-4a2c-bc30-04b5c7a8d79b · outbound

This paper cites Private reinforcement learning with pac and regret guarantees.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Private reinforcement learning with pac and regret guarantees

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:16.869511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.133977Z digest=sha256:eb1eb553c5c7f2d1150836ab927de8ee5109a9ad006a0c288eb399d8ef090bc9

Observation 36692d03-6505-42c3-b6d9-03a864a07656 · outbound

This paper cites Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:16.765612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.199802Z digest=sha256:ee077aada5e889442502d3238eeac58236bfedd767656af7de85490b2a67ef60

Observation 7540fe80-4775-4f29-b38b-60eae47e4d60 · outbound

This paper cites Stop-and- Go : Exploring Backdoor Attacks on Deep Reinforcement Learning -based Traffic Congestion Control Systems.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Stop-and- Go : Exploring Backdoor Attacks on Deep Reinforcement Learning -based Traffic Congestion Control Systems

Reference 55

Resolution
metadata mismatch
raw_fallback, observed 2026-08-07T13:58:14.155163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.248103Z digest=sha256:587f616a9301e352945fc39783e04cdd363bebc32c32e44cf713df73aeb671da

Observation ba3d32fb-2bb7-4e76-b43a-df73ff573454 · outbound

This paper cites COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning Attacks

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:13.290928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:13.290928Z digest=sha256:0a05fba83a724714a387a1e2a13fae9a9e078bb852e552ffa8592252eb25ca19

Observation 5ffef097-7597-4449-a8d6-5b5d98e75068 · outbound

This paper cites Reward Poisoning Attacks on Offline Multi-agent Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Reward Poisoning Attacks on Offline Multi-agent Reinforcement Learning

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:16.665669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.342851Z digest=sha256:cf7864f9f701ced48d7fadd1dcedcbc7f192166c0cb054d2064afbc857eb975e

Observation ed62ba83-6f84-4164-82d6-76bbb48ffb17 · outbound

This paper cites Towards Robust Offline Reinforcement Learning under Diverse Data Corruption.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Towards Robust Offline Reinforcement Learning under Diverse Data Corruption

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:13.401469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:13.401469Z digest=sha256:e9d7e654a06ca31bcd72d05e948f2266b96b1dc605c0ae02cd3e35e6130dd9ca

Observation 38072f5c-ea6b-4682-933a-7422a2fc3004 · outbound

This paper cites Corruption- Robust Offline Reinforcement Learning with General Function Approximation.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Corruption- Robust Offline Reinforcement Learning with General Function Approximation

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:16.482507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.431278Z digest=sha256:8b35f655d5b9c30f9ad19ce41512bd5571cc0b5e9f8ce1a2e083fccf9039549a

Observation e3968089-bdcd-4e9b-968a-9028aae86aa0 · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:13.486506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:13.486506Z digest=sha256:93671cdabfeff16fe83271e3afa45f22a90ecca08a890f625d46f53f48d6decf

Observation e96d09a9-bedc-43f1-9864-8a61614da9e8 · outbound

This paper cites Reinforcement Learning in Healthcare: A survey.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Reinforcement Learning in Healthcare: A survey

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:58:16.261783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.537023Z digest=sha256:1e92a4c12d57cd8c49108e2a078ca34f47957e33d748138f14e3e0e59a16d00b

Observation fb03fb0a-7a4f-41ed-bd17-e3c4c8229bfd · outbound

This paper cites Corruption-Robust Offline Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Corruption-Robust Offline Reinforcement Learning

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:13.577413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:13.577413Z digest=sha256:c460163bb121f5231a002cc1f44b21778ac2781d317145eed544d07e00dd73a5

Observation 89a41442-0c24-4b8a-9c40-0b924e3174c9 · outbound

This paper cites Robust Policy Gradient against Strong Data Corruption.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Robust Policy Gradient against Strong Data Corruption

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:13.612803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:13.612803Z digest=sha256:7f3db49a6fc2dffa69b39e9e78c3147363f3e488535b877eb3e7ac2ae57794c8

Observation 14cb29f9-f190-415d-ba4b-f112960a727a · outbound

This paper cites @esa (Ref.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning @esa (Ref

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:13.652960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:13.652960Z digest=sha256:1c2368d39ca0b0a838cd79216822ce81bd8e131c4c5efc10ed6e864d79fda3d6

Observation d0afb212-4af5-448f-ad11-7350dcd94b20 · outbound

This paper cites an unresolved cited work.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Unresolved cited work

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-07T13:58:13.720597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:58:13.720597Z digest=sha256:56f82ca64d15bf700232331d1240b99b054d62515664f60454c575f49217fcd5

Observation e3447457-77e2-4a75-a7c8-d454236472f4 · outbound

This paper cites Locally Private Distributed Reinforcement Learning.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning Locally Private Distributed Reinforcement Learning

Reference 66

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T13:58:14.432828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-07T13:58:13.783532Z digest=sha256:bb840ea5e2aa2bee307d4f9af5ce1499e9cf8896d2f03535f7f3453f92881025

Pith citing papers

No inbound Pith citation observations are available.