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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=sha256:1ab211219bc0a95d7d37fcd37d1555319f11d4ed9b5c7c2850c6ed42805e4a29

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=sha256:80a63c18e36c1a40218f3d0d665bb7e279ea566267bbf0213c33cce993bf41d9

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:e6921ae62658f1d6680cee548f0306a32a42715edbda5635cfd0ad0a1e88abb0

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=sha256:941a788b9c9227395f4ce708f32ec434afd7998649fe0d409157f760d89f7ced

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=sha256:9b773e2c3b2e4e18552b7ee514e666b7aa5e18efdb7fb8f0b164da31ba5ebc22

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=sha256:1051417d4b521876fd54258e46c16028ee7607c0912348c70e178224db116585

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=sha256:2f505ae7e95c994ed54f503cb75ec058af5c669b3ee8c9fb6bfb457be4f0b698

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=sha256:08addd8b9c083e4562a22166d34a2216dee8e6f644a5ef7bb0551c60218ed567

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:a5104d3f4bb34b2647ca1c3b2a05a72c58f944039d786170d707223393ea9439

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=sha256:99a3a3e162c2f3ffef9eb151db76caeff57d8b3446129f6ca597adbc28bfded9

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:ac320a478e3a910d8f35e71ac4cb5a12f8cd6576be6d9744108e2746c2283cb3

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

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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:e555b3c2fb02f52694130385b4c3a0205d65087e6776ecf1b78caa64bba8f1e8

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:c06e54fe6be76f8f8556323efa2ec48c4b63f67c066535c5b2585fd63b7a4781

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=sha256:9bcfe91eb4fa60d85490857ed25723af44e23006d9a9db4302e1865899d9e384

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=sha256:340e0adcc988d01268c5519e9893917dec8aad514cf0ef6ee3bde66d96da5f7d

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=sha256:8a08f17a67c236b04d4f5503dc15e6a0b304940c1ab3cded9938d06515758585

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

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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:e9370ade31ab904b274c684fa323d8f4e4f0d4c97bd8f7b7db816d9d5d7ff292

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:aae87991060e97d9652b3c5a53a6f06393c89b994bda8f6af753d53a7d74078b

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:e40e779cdd654ce3e24fdc3094b4ac3ee2d83ac03eba0d00560c40848d607665

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=sha256:0f09c082854d1ee6dce0436c8426b025aee84344fb166dbd7f4d72a269ed682d

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=sha256:1e563d0d03dd83ea07ecba1aa461cedde9b1a89644453a04e0d37ebb70fa10c7

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=sha256:701da96306274dd21f88f53b9a50c91650399d679605dccb03fe4efecca53452

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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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=sha256:0dfad7e01cefa3761b236428fe5af807ef0b09a28bf8286bba2fe523ad1eda4d

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:a94c0b8136c8976d87c03281dbfc230e1ecb0adb07140380dd401b9999c753f8

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=sha256:00d8ddf81a20ef8aa467614011518bd365c21913a5da083f090b53d47e99a034

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:f037773125e318ac94bcb07827fa8e35f26c1a2d225c06aeb7a8b2a6ad73970d

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=sha256:51103c8fbe0f4136051f0020fb8aa0f220a8ae88d89be52f7a13dac1fc96fa7c

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:eac9f3614a88b0ecfab322d1d3d5259173fc6d24fd6df69ee5a990bcc3e1183f

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=sha256:25b9c07bfcdfc3b7dc6cd997fcec05db6a67c1c3974465f9d495519cff6e3103

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=sha256:1d49883c0d3265aa898dc4362eb858c68bc701281d434a9dc410f05c8886a88b

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=sha256:4c46c3aff13cde4720b32ae553ee484b6d7f3729731c82119c4f3ef4fd992614

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:cb9cd5400abe5d9986506a11ba7d124a84520cfcbc613143e7e820eac8c7dbb9

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:ac270b8eae141afc689de5a98dc596da8f470abfc218bfdc8057af22fa41386e

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=sha256:307fd2ce16b903de9475298726f66de9bdf77eb98abcdd3cd5435927f795a7a1

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=sha256:2dedfc450288700017f04b86393211a19caa7b3b81a635eee37b21392ce685dc

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=sha256:853a186c08772bbd8e55537fb9c34725f2b7c48e4127da36e631517a14802edc

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=sha256:2f9bfe6ddad264443b1d7025dbf9c7f283700d18f1e7dd805b1bca0cd6dfb2bc

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=sha256:4e048bae0baf7e26bdf3c8609217570cfb503988cc7a91263c5354783b746857

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=sha256:8b7b86adb5d853147bf41156e400d04de768184642739abc0df16de277735754

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=sha256:13825b9384c3f7378ff70159b186c88f2cbca57005bafc8e02c059709d8ef986

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:a8b0f80fe5ca60a9d50a1f157a3fea82d851fea626a78309208c8c68b0858842

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=sha256:72d9234f3c5027d830c82c85c1729d6a2c55b4e6bb6d9d482d784505a1295e25

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=sha256:9fd7e34c8ed9e3dd258066c8e0d4cc48a4154e790274e1d7536e2a7cd4c9258c

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:d79f76258be28549e51b5e9ad38a9a0a792e2632008a062680d73999d98aad8a

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=sha256:26f59741830254d6094774cf02e2cb85f2a4d81176aa2798659f6abc10d9d490

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=sha256:5054b9daa113c68e91b07b8c035ee9cc994f58275d804ebe37c6a541d2eb5714

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=sha256:48a16152be61dc26e9cb8346dcf29a338fdd55ddc084c48a78b8809249be9d02

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=sha256:967c38053e663e0ca773d282b8329bb9f9f727adabe08d17e3cd2325cdd760fd

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=sha256:9d6846a2e50a5308911d5615d7320b4bface9aff05d038176f084ee413af7d68

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=sha256:323123e33260cc7b8a7c20344f18a9c07df3303461b10ef8513736fb111d678a

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:fc3f2e9e9ca96a473ed385d413eccfdaac2ea8c656f4d03360a68112cb717550

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:ef18d34039ab03c3fc7b23d525455f5c8cc9c660e2698032b0c626dbdb604d12

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=sha256:164565b882fb7b3c289f1747dae4eaee7efac37fa87bd21d5299df946626cf6a

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:d5b8ea9671e8aa2d12ed99ebe79ec115d7742410e6acc667f5d1d5377234d0b7

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=sha256:2004996f4eae846efbe6c5d6ee2a0c03409e8ac3f4fb968882fb96d86dcac8ae

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:ba3c2658b2a61c8ea39c82e470ee9d4d4b23a5d22f1c74baf5cae7f4b7735a23

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=sha256:31cc1f0c8d0340784edb0b1a1f0636a3b94a686b2e37775ea76db032e26c9c94

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=sha256:095b168e92864a89b6ac70f6d3bfdc835d38d8f123e18106af0bd6ab677c7d80

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=sha256:77c0fa10aaea33fb5268413af6f5c363c442aeaae11ee2cbf80477d7327efa5f

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=sha256:6b341a176853f465060757a086d5aa44a6d10a6b033f310e44777dd0f69203f3

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:bb651307785f4a08e0ce46ab3a82d7910920696141231b102b3bc4b7a1e642eb

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=sha256:3ec8e1434edc0e915c1ed325044a76591850e7e9daf91aaeecf5da080742b4e6

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=sha256:425c40453297f0a462033e14e920df9ad439b1ea83474f49cee20a59f0499591

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=sha256:27e7801af8c422afd1710dcbb2dedbd95e321b9695a1a6dfff3bcf5210eeab0c

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=sha256:719515e023f5bcc2974ebacace0ed123a978082e2870cbf0e06664bb7222de24

Pith citing papers

No inbound Pith citation observations are available.