Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation | Paper Citation Record

Observation bb7ff99d-7ef7-49db-bbb1-3787136c11a8 · outbound

This paper cites National health interview survey,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation National health interview survey,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:13.410423Z

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Observation 7a997bd5-5e2d-482b-95f4-a2f559e29369 · outbound

This paper cites Learning to Ask Medical Questions using Reinforcement Learning,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Learning to Ask Medical Questions using Reinforcement Learning,

Reference 2

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Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Unresolved cited work

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This paper cites A survey on deep learning in medical image analysis,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation A survey on deep learning in medical image analysis,

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This paper cites An overview of clinical decision support systems: benefits, risks, and strategies for success,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation An overview of clinical decision support systems: benefits, risks, and strategies for success,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:12.894946Z

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This paper cites An adaptive testing item selection strategy via a deep reinforcement learning approach,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation An adaptive testing item selection strategy via a deep reinforcement learning approach,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:12.747514Z

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Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Unresolved cited work

Reference 7

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Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Bellman,A Markovian decision process

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:12.570591Z

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This paper cites an unresolved cited work.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Unresolved cited work

Reference 9

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Observation 4df0ee94-47fa-4ea5-a0e0-3c15520cdeb1 · outbound

This paper cites Adversarial attacks on medical machine learning,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Adversarial attacks on medical machine learning,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:12.369860Z

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Observation ce23a4b0-9be1-49a2-a687-c2c985621d77 · outbound

This paper cites A Marauder's Map of Security and Privacy in Machine Learning.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation A Marauder's Map of Security and Privacy in Machine Learning

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Resolution: verified exact; local_arxiv, observed 2026-08-06T04:37:07.557164Z

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Observation 672cd86a-f3be-44a9-a3bf-72da94cf414b · outbound

This paper cites Evasion attacks against machine learning at test time,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Evasion attacks against machine learning at test time,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:12.193831Z

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Observation 388f4614-9801-4fbb-ad67-99d36fd54c5b · outbound

This paper cites The security of machine learning,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation The security of machine learning,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:12.052475Z

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This paper cites Concrete Problems in AI Safety.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Concrete Problems in AI Safety

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Observation a7e3f640-8f40-4d67-b1c9-2cdd0cc72bd0 · outbound

This paper cites Russell,Human compatible: Artificial intelligence and the problem of control.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Russell,Human compatible: Artificial intelligence and the problem of control

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:11.862991Z

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This paper cites COM(2021) 206 final.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation COM(2021) 206 final

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:11.738892Z

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This paper cites LOINC: Logical observation identifiers names and codes.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation LOINC: Logical observation identifiers names and codes

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:11.603011Z

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Observation 71fc4aad-50a0-43c4-b330-9b296b6c1848 · outbound

This paper cites SNOMED CT: Systematized nomenclature of medicine clinical terms.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation SNOMED CT: Systematized nomenclature of medicine clinical terms

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:11.165427Z

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Observation 629828db-d9d9-416f-8f3e-38173be1f733 · outbound

This paper cites ICD-11: International classification of diseases 11th revision.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation ICD-11: International classification of diseases 11th revision

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:10.963317Z

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Observation 3606a36b-18c4-40c2-a17e-1e489df5b4ec · outbound

This paper cites Standards of care in diabetes-2025,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Standards of care in diabetes-2025,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:10.739644Z

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Observation 68b37a9c-a575-49fa-a205-561e5a3f24aa · outbound

This paper cites Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:10.553792Z

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Observation 62b12bee-72a8-48fb-bfc8-b0419072519a · outbound

This paper cites Intriguing properties of neural networks.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Intriguing properties of neural networks

Reference 22

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Observation 1dd1f138-fe81-497d-a8bd-a9d9c5141297 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Explaining and Harnessing Adversarial Examples

Reference 23

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This paper cites Towards deep learning models resistant to adversarial attacks,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Towards deep learning models resistant to adversarial attacks,

Reference 24

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This paper cites Practical black-box attacks against machine learning,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Practical black-box attacks against machine learning,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:10.309950Z

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Observation 1daaa96d-c3c4-4f49-8581-97fe808039ab · outbound

This paper cites Delving into transferable adversarial examples and black-box attacks,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Delving into transferable adversarial examples and black-box attacks,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:10.138753Z

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Observation 6a60fa5f-13d5-4f12-8922-9d2774cb10a4 · outbound

This paper cites Black-box adversarial attacks with limited queries and information,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Black-box adversarial attacks with limited queries and information,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:09.923582Z

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Observation 81ecf6ad-7048-4ba1-8917-d5336587734e · outbound

This paper cites Autozoom: autoencoder-based zeroth order optimization method for attacking black-box neural networks,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Autozoom: autoencoder-based zeroth order optimization method for attacking black-box neural networks,

Reference 28

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Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:09.567046Z

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Observation 303f28b5-2625-4cd4-8e8a-29baf3be5227 · outbound

This paper cites Adversarial Attacks on Neural Network Policies.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Adversarial Attacks on Neural Network Policies

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Observation babecf3e-e3f9-4818-8682-b7b87f483782 · outbound

This paper cites Tactics of Adversarial Attack on Deep Reinforcement Learning Agents.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Tactics of Adversarial Attack on Deep Reinforcement Learning Agents

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This paper cites Adversarial Policies: Attacking Deep Reinforcement Learning.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Adversarial Policies: Attacking Deep Reinforcement Learning

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This paper cites Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information

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Resolution: verified exact; local_arxiv, observed 2026-08-06T04:37:07.244651Z

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Observation a869f06c-b7ad-4480-94a1-937fddda9a3e · outbound

This paper cites Robust reinforcement learning via adversarial training with langevin dynamics,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Robust reinforcement learning via adversarial training with langevin dynamics,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:09.379211Z

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Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Bayesian learning via stochastic gradient langevin dynamics,

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This paper cites Understanding deep learning requires rethinking generalization,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Understanding deep learning requires rethinking generalization,

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This paper cites Understanding adversarial attacks on deep learning based medical image analysis systems,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Understanding adversarial attacks on deep learning based medical image analysis systems,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:08.847605Z

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Observation 7d2ada32-f789-49b6-9523-4ec96b67767e · outbound

This paper cites Impact of adversarial examples on deep learning models for biomedical image segmentation,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Impact of adversarial examples on deep learning models for biomedical image segmentation,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:08.685054Z

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Observation c2801781-06ae-48df-9201-91d70c71f993 · outbound

This paper cites A hierarchical feature constraint to camouflage medical adversarial attacks,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation A hierarchical feature constraint to camouflage medical adversarial attacks,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:08.514920Z

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Observation f926b9bc-a427-46c1-a779-8dfba63d018b · outbound

This paper cites The power spectrum and structure function of the Gamma Ray emission from the Large Magellanic Cloud.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation The power spectrum and structure function of the Gamma Ray emission from the Large Magellanic Cloud

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Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T04:37:07.045639Z

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Observation 3cfb78d9-6b52-4c3c-a640-75bb8b7d012b · outbound

This paper cites Towards evaluating the robustness of neural networks,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Towards evaluating the robustness of neural networks,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:08.354115Z

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This paper cites Human-level control through deep reinforcement learning,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Human-level control through deep reinforcement learning,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:08.236275Z

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Observation 6a23aa29-8572-416a-ac1d-6128517be420 · outbound

This paper cites Self-improving reactive agents based on reinforcement learning, planning and teaching,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Self-improving reactive agents based on reinforcement learning, planning and teaching,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:08.057841Z

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Observation ad573df7-6696-4a19-b923-4d9150081eb4 · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks,.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Understanding the difficulty of training deep feedforward neural networks,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:37:07.826680Z

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Observation 7561827b-15e0-4478-ae5b-371c0bc03a63 · outbound

This paper cites Limits of Deepfake Detection: A Robust Estimation Viewpoint.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Limits of Deepfake Detection: A Robust Estimation Viewpoint

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Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T04:37:06.741360Z

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Observation 2ee305a0-09b1-42d2-ab48-41d9b810d86f · outbound

This paper cites an unresolved cited work.

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-06T04:37:11.387092Z

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