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Paper Citation Record · LEDGER

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization

As of 18 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2412.19578.

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

pith.paper-citation-record.v1
2412.19578 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:16:23.971894Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

48 of 48 outbound references displayed

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  • verified fuzzy37
  • unresolved11
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3a3a2549-fe46-41d2-a711-f113477c7620 · outbound

This paper cites ’virus and epidemic’: Causal knowledge activates prediction error circuitry,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization ’virus and epidemic’: Causal knowledge activates prediction error circuitry,

Reference 1

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Observation ff955741-3309-44b1-b9e1-f731c2592a0e · outbound

This paper cites Causality for Machine Learning.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Causality for Machine Learning

Reference 2

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Observation c8541e12-0dbc-4d63-866b-0ed72ad9b053 · outbound

This paper cites From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data,

Reference 3

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Observation 8c4a33a6-91c8-48a5-9e08-398dda95d2bc · outbound

This paper cites Learning bayesian networks is np-complete,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Learning bayesian networks is np-complete,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1b896bf6-d886-47a0-bcdb-b22fd0ac4db3 · outbound

This paper cites Causal discovery with reinforcement learning,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Causal discovery with reinforcement learning,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 9371cffc-bcf2-452d-91f5-64dcaf609c99 · outbound

This paper cites Estimating the Dimension of a Model,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Estimating the Dimension of a Model,

Reference 7

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Source-reported events for the cited work

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Observation febd5bc0-497b-4bfc-9543-2341586f2a86 · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Simple statistical gradient-following algorithms for connectionist reinforcement learning,

Reference 8

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Source-reported events for the cited work

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Observation 52fcbfc4-dfbe-4116-be2f-c41d1f31ad12 · outbound

This paper cites Policy gradient methods for reinforcement learning with function approxima- tion,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Policy gradient methods for reinforcement learning with function approxima- tion,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 45ad2c72-b893-4585-8bdc-0b7879068ebd · outbound

This paper cites Trust region policy optimization,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Trust region policy optimization,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 8c17b5e1-f94a-4102-b573-00f3be016a21 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Proximal Policy Optimization Algorithms

Reference 11

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Source-reported events for the cited work

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Observation 184215e8-a1fe-4b89-bc9b-cde8757cdc05 · outbound

This paper cites Graph Attention Networks.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Graph Attention Networks

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e6086f89-8ebf-4d1b-b1a5-6b4ff46dc59f · outbound

This paper cites Approximating discrete probability distri- butions with dependence trees,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Approximating discrete probability distri- butions with dependence trees,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation fb9157af-2389-48e6-9988-c8615c6d072e · outbound

This paper cites The max-min hill- climbing bayesian network structure learning algorithm,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization The max-min hill- climbing bayesian network structure learning algorithm,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 56705c56-326f-447a-9fce-736049671ca8 · outbound

This paper cites Dags with NO TEARS: continuous optimization for structure learning,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Dags with NO TEARS: continuous optimization for structure learning,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 2edb3afa-cd42-42fb-ab5d-63000d4ea8ae · outbound

This paper cites Generalized score functions for causal discovery,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Generalized score functions for causal discovery,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 0eb3bfb3-03a4-4702-91bd-6a9c8432d952 · outbound

This paper cites Causal discovery with continuous additive noise models,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Causal discovery with continuous additive noise models,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6722d0b7-3f4a-4547-9fca-4b095dc4e057 · outbound

This paper cites A machine learning approach to classify pedestrians’ event based on imu and gps,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization A machine learning approach to classify pedestrians’ event based on imu and gps,

Reference 18

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Source-reported events for the cited work

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Observation 59d5b341-eccd-428b-b1a4-bedb5059e479 · outbound

This paper cites Deep learning versus traditional solutions for group trajectory outliers,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Deep learning versus traditional solutions for group trajectory outliers,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3cb29365-e3df-43ec-84c6-f0e78576ddb2 · outbound

This paper cites Iterative feedback and learning control. servo systems applications,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Iterative feedback and learning control. servo systems applications,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation e9db1719-3ca3-495b-a130-1d0e5326fa47 · outbound

This paper cites Adaptive ekf-based vehicle state estimation with online assessment of local observability,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Adaptive ekf-based vehicle state estimation with online assessment of local observability,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation e89725c7-e918-4c75-a222-980e875ec994 · outbound

This paper cites Learning functional causal models with generative neural networks,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Learning functional causal models with generative neural networks,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 1c418afc-5b1b-461c-9e96-ac40a2a536df · outbound

This paper cites Structural Agnostic Modeling: Adversarial Learning of Causal Graphs,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Structural Agnostic Modeling: Adversarial Learning of Causal Graphs,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation e9b72fbe-e06e-449c-8128-b9d876458032 · outbound

This paper cites DAG-GNN: DAG structure learning with graph neural networks,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization DAG-GNN: DAG structure learning with graph neural networks,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 0f473df5-fa80-4f7f-b040-65900fe8814a · outbound

This paper cites A new model for learning in graph domains,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization A new model for learning in graph domains,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 609e598e-6526-44aa-aff5-e6f6b2b2edb2 · outbound

This paper cites The graph neural network model,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization The graph neural network model,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation cdb8993d-0427-4a39-9310-7688354fd097 · outbound

This paper cites ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ea85c78e-b1f0-48e1-a9fc-850a0fb285df · outbound

This paper cites Inductive Representation Learning on Large Graphs.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Inductive Representation Learning on Large Graphs

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f56e34bf-35e4-4d1b-abc5-b1e6c98e4e45 · outbound

This paper cites Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8b7a1b97-585c-4403-a955-c9ca5c932d3d · outbound

This paper cites Survey of model-based reinforce- ment learning: Applications on robotics,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Survey of model-based reinforce- ment learning: Applications on robotics,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 5339c8af-fe21-4e11-b91d-bfb4bec52064 · outbound

This paper cites Online rein- forcement learning control for the personalization of a robotic knee prosthesis,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Online rein- forcement learning control for the personalization of a robotic knee prosthesis,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 38a35f82-5f20-4d49-89a4-fe5136b40840 · outbound

This paper cites Multitask learning for object localization with deep reinforcement learning,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Multitask learning for object localization with deep reinforcement learning,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation aee59455-4580-4899-9bc9-b303bbb0aea3 · outbound

This paper cites Nonzero-sum game rein- forcement learning for performance optimization in large-scale industrial processes,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Nonzero-sum game rein- forcement learning for performance optimization in large-scale industrial processes,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f9d4505e-ff9a-4a2f-b33b-b3833940fd93 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Neural Architecture Search with Reinforcement Learning,

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 98a6976b-4d6f-402b-b7b4-c5f14421a943 · outbound

This paper cites an unresolved cited work.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Unresolved cited work

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a76beb80-dfa6-4983-8d2f-702bdaa43b3d · outbound

This paper cites Spirtes, C.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Spirtes, C

Reference 36

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raw_fallback, observed 2026-08-11T00:16:24.694556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.912515Z digest=sha256:2436a6d50dfdc060132c2e2ad16ccf7ce9369a936f5a568a6fbcacd47e8510df

Observation 13e7c7cf-7f9d-41bd-b1b5-e513c31f0021 · outbound

This paper cites A linear non-gaussian acyclic model for causal discovery,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization A linear non-gaussian acyclic model for causal discovery,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.276812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.917531Z digest=sha256:38db9200cae7a3322cc6b0471af125ba13e15cdff62c637957af820fd90772cf

Observation 5d600eda-9ff7-4f28-b96d-24d767b2cd0e · outbound

This paper cites On the Prop- erties of Neural Machine Translation: Encoder-Decoder Approaches,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization On the Prop- erties of Neural Machine Translation: Encoder-Decoder Approaches,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.262464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.922413Z digest=sha256:7fa95b7dc6374dadcb4e3b290fb62acd730a0472b819734db49108c9f7534f86

Observation 98d03a7e-f24b-4d1a-87ae-58060d2c65ff · outbound

This paper cites Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.248028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.927112Z digest=sha256:4f179d23a255c1eb2b8f193caf1a8401386f46c257ad582b74c71d71a4461ebb

Observation de8ac5a4-6dd7-4a42-b845-f7f3a4f9eb4f · outbound

This paper cites Function optimization using connectionist reinforcement learning algorithms,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Function optimization using connectionist reinforcement learning algorithms,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.233241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.931745Z digest=sha256:083f72c5f5415768fcc794007544892e32b6d1e92d83e8abcbc441e186de0d7c

Observation d7a64fa6-b5cb-4c63-8a72-3d9c215496fd · outbound

This paper cites Prioritized Experience Replay,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Prioritized Experience Replay,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.217630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.935842Z digest=sha256:1cfd843c76e33bcb98b2cce3193e5d5a5237543d02a64cd5716f5b344c4590fc

Observation 02c310d8-ef51-42ea-8c21-7bb9d3055930 · outbound

This paper cites A Closer Look at Deep Policy Gradients.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization A Closer Look at Deep Policy Gradients

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T00:16:23.939980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:16:23.939980Z digest=sha256:3b8b7f6949e489fc79ee6f10e518f446cb74f034c73013e5dbeff8883f07fcd3

Observation b2eb6b1b-3ba7-47a6-9d6d-2b4ae0450792 · outbound

This paper cites Ramsey, M.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Ramsey, M

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.200418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.944847Z digest=sha256:d64b1ec9c82723a3859afac8e8470d76f0362887efccce55619f6496321a1087

Observation 5ebf8539-b350-4a29-965e-01deff009540 · outbound

This paper cites CAM: Causal additive models, high-dimensional order search and penalized regression.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization CAM: Causal additive models, high-dimensional order search and penalized regression

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T00:16:23.949439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:16:23.949439Z digest=sha256:b3226834a205f30881ee92e655ee866bc5dd56fb7802a704da8d81e9bdb51864

Observation 97fad022-e43c-4b5c-8b24-1cbe66d64b81 · outbound

This paper cites Gradient-Based Neural DAG Learning.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Gradient-Based Neural DAG Learning

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T00:16:23.954516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:16:23.954516Z digest=sha256:58d316224c6f36267367204f48a9dfdfb970b4c08154996dd5a130af1da5d6e0

Observation 8b2f416f-b4b3-42a9-861f-8425f32f40f5 · outbound

This paper cites Causal Protein-Signaling Networks Derived from Multiparam- eter Single-Cell Data,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Causal Protein-Signaling Networks Derived from Multiparam- eter Single-Cell Data,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.184311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.959228Z digest=sha256:7a2c6ab89d552e8c2595328247d29db44e0eebfc0a0a1b27a9fe547562e3dff2

Observation 0f88161c-039b-4e44-aea1-4696ba564556 · outbound

This paper cites Syntren: a generator of synthetic gene expression data for design and analysis of structure learning algorithms,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Syntren: a generator of synthetic gene expression data for design and analysis of structure learning algorithms,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.168558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.963624Z digest=sha256:fd68e2471bff51acb117b7f0e40148895483b41a02e8c96bb38f11ab2dfcc2e7

Observation 24123bbd-aeaa-45af-9ae8-15a5b36f1df0 · outbound

This paper cites Truly proximal policy optimization,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization Truly proximal policy optimization,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.153034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.967896Z digest=sha256:dba0a224e822059d440c759a3a3811bf79e4cfecbac4069f917861d769130856

Observation 5991fe42-5bed-484d-80b2-56666ca8adec · outbound

This paper cites A hybrid method for nonlinear equations,.

Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization A hybrid method for nonlinear equations,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T00:16:24.136319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T00:16:23.971894Z digest=sha256:a0660896db5364055a7464df3ddfd86007fd27549d0fd608953b6d5aee89a7fb

Pith citing papers

No inbound Pith citation observations are available.