Paper Citation Record · LEDGER

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2501.10529.

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

pith.paper-citation-record.v1
2501.10529 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:13:44.825132Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation f3f7db56-4932-4d82-a4fd-a44250e8df74 · outbound

This paper cites GPT-4 Technical Report.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning GPT-4 Technical Report

Reference 1

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Observation d1ba1d31-b131-4d34-aaf2-44e0f9f02c80 · outbound

This paper cites Meta-learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Meta-learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:13:45.177712Z

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Observation 164c0cf3-52ec-4b59-b54f-fd742a482982 · outbound

This paper cites Meta-Learning in Neural Networks: A Survey.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Meta-Learning in Neural Networks: A Survey

Reference 3

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Observation f23c165b-928c-45da-af10-cadb85e100de · outbound

This paper cites Probabilistic model-agnostic meta- learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Probabilistic model-agnostic meta- learning,

Reference 4

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Observation 6d3f7210-6df2-4d27-a5b4-0a7426b997f8 · outbound

This paper cites Efficient off- policy meta-reinforcement learning via probabilistic context variables,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Efficient off- policy meta-reinforcement learning via probabilistic context variables,

Reference 5

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Observation aa77302f-f7fc-435a-b3af-8de7fa95dc19 · outbound

This paper cites Multitask learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Multitask learning,

Reference 6

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Observation 3d96667f-c88d-42fe-b7f6-ae12289de9da · outbound

This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning An Overview of Multi-Task Learning in Deep Neural Networks

Reference 7

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Observation a8b1c411-c363-4b53-8fbe-ce60983baabb · outbound

This paper cites A survey on multi-task learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning A survey on multi-task learning,

Reference 8

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Observation dbeca252-173d-4b89-a973-028c0bdb2bcb · outbound

This paper cites Multi- task reinforcement learning in reproducing kernel hilbert spaces via cross-learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Multi- task reinforcement learning in reproducing kernel hilbert spaces via cross-learning,

Reference 9

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Observation e5a367fb-66fc-4807-b934-2be2ba116cec · outbound

This paper cites Multi- task supervised learning via cross-learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Multi- task supervised learning via cross-learning,

Reference 10

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Observation 6afb0fdd-4cb4-455c-9205-8825b6bef795 · outbound

This paper cites Multi-task learning via conic programming,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Multi-task learning via conic programming,

Reference 11

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Observation 3ae34d80-5428-41d8-9e43-0ae23414f155 · outbound

This paper cites Conic programming for multitask learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Conic programming for multitask learning,

Reference 12

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Observation 67b5ed59-4034-4593-a9dc-e55681c52972 · outbound

This paper cites Proximity without consensus in online multiagent optimization,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Proximity without consensus in online multiagent optimization,

Reference 13

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Observation 6f713957-a026-46f8-8dbd-8692b3d8cfc8 · outbound

This paper cites Parsimonious online learning with kernels via sparse projections in function space,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Parsimonious online learning with kernels via sparse projections in function space,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:13:45.067066Z

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Observation ce5de5dc-6ebc-41ad-8ae1-ededf9251501 · outbound

This paper cites Learning to Multi-Task by Active Sampling.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Learning to Multi-Task by Active Sampling

Reference 15

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Observation ec84a5b6-e684-4f28-9512-002a4b1077bd · outbound

This paper cites Multi-task learning as multi-objective opti- mization,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Multi-task learning as multi-objective opti- mization,

Reference 16

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Observation 3c7f3b57-bf7e-416e-835c-3c4383c3ae3a · outbound

This paper cites Regularized multi–task learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Regularized multi–task learning,

Reference 17

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Observation 90b99c83-5fb9-4c55-921c-ea3d8a9cf214 · outbound

This paper cites A convex formulation for learning task relationships in multi-task learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning A convex formulation for learning task relationships in multi-task learning,

Reference 18

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Observation be3d97f8-3944-4ef7-b5c8-39d8d4ee9954 · outbound

This paper cites Transfer learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Transfer learning,

Reference 19

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Observation 2563645a-53bf-4435-899d-29815e54bfd4 · outbound

This paper cites Matrix low-rank approximation for policy gradient methods,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Matrix low-rank approximation for policy gradient methods,

Reference 20

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Observation 119dda7a-8a50-47f0-a1d3-07a82d216e3b · outbound

This paper cites Tensor and matrix low- rank value-function approximation in reinforcement learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Tensor and matrix low- rank value-function approximation in reinforcement learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:13:44.997844Z

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Observation 3648a9a1-2015-4e9c-980c-6873d4e841c4 · outbound

This paper cites Tensor low-rank approximation of finite- horizon value functions,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Tensor low-rank approximation of finite- horizon value functions,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:13:44.986714Z

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Observation 7f6e54c9-92ae-43cd-a66e-e3d1dad4e064 · outbound

This paper cites Q-learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Q-learning,

Reference 23

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Observation 5a8cc887-e3a9-4d7c-bf57-44bc3637e6f0 · outbound

This paper cites Linear least-squares algorithms for temporal difference learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Linear least-squares algorithms for temporal difference learning,

Reference 24

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e3766859-1d39-4155-884c-467ef79721da · outbound

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

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Human-level control through deep reinforcement learning,

Reference 25

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Observation 194bcbb3-a917-4d22-bda5-13bafa59cff1 · outbound

This paper cites Proximal Policy Optimization Algorithms.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 26

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Source-reported events for the cited work

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Observation a3d9d162-a95e-45c7-950b-ef7189124d49 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 27

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Observation e9261cb6-42f2-4391-97b0-1576f134e0c6 · outbound

This paper cites Tensor decomposition for signal processing and machine learning,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Tensor decomposition for signal processing and machine learning,

Reference 28

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Observation 960d7ecc-e05f-4cf6-a3d7-cf8e6e7e1ae8 · outbound

This paper cites Algorithmic survey of parametric value function approximation,.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning Algorithmic survey of parametric value function approximation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:13:44.938331Z

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Observation 34d9ffa5-5147-4cf8-9d80-06463a1021cc · outbound

This paper cites OpenAI Gym.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning OpenAI Gym

Reference 30

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Source-reported events for the cited work

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Observation ac35ea83-5d45-4427-af23-bb9f68cb140e · outbound

This paper cites A tensor low-rank approximation for value functions in multi-task reinforcement learning.

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning A tensor low-rank approximation for value functions in multi-task reinforcement learning

Reference 31

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Source-reported events for the cited work

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