Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning | Paper Citation Record

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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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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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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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

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

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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

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

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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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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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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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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

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

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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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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

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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

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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

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

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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

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

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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

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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

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

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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

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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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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

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

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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

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

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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

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

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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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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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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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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

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

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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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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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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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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

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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 2026-08-08T06:32:00.761636+00:00.

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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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

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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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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

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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

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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

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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

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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

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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

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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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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

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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 2026-08-08T06:32:00.761636+00:00.

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

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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 2026-08-08T06:32:00.761636+00:00.

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

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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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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

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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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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.

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 2026-08-08T06:32:00.761636+00:00.

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.

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 2026-08-08T06:32:00.761636+00:00.

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.

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 2026-08-08T06:32:00.761636+00:00.

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.

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.

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.

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.

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 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers