Paper Citation Record · LEDGER

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation

As of 20 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2501.10598.

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

pith.paper-citation-record.v1
2501.10598 v3

Coverage vector

measured 53 of 53 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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

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Reference resolution

53 of 53 outbound references displayed

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External citation measurements

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Outbound references

Observation 7cbeb599-8817-4b86-af64-6529ed441302 · outbound

This paper cites Bertsekas, Dynamic programming and optimal control: Volume I, vol.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Bertsekas, Dynamic programming and optimal control: Volume I, vol

Reference 1

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Unresolved cited work

Reference 2

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Unresolved cited work

Reference 3

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This paper cites Bertsekas, Reinforcement learning and optimal control , vol.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Bertsekas, Reinforcement learning and optimal control , vol

Reference 4

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This paper cites Mastering the game of Go with deep neural networks and tree search.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Mastering the game of Go with deep neural networks and tree search

Reference 5

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This paper cites Mastering the game of Go without human knowledge.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Mastering the game of Go without human knowledge

Reference 6

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This paper cites Language models are few-shot learners.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Language models are few-shot learners

Reference 7

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This paper cites Dynamic programming.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Dynamic programming

Reference 8

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Unresolved cited work

Reference 9

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Observation fefa1180-1b72-48c8-a80c-e3a4e3dbe70f · outbound

This paper cites Bertsekas, Neuro-dynamic programming.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Bertsekas, Neuro-dynamic programming

Reference 10

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This paper cites Least-squares policy iteration.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Least-squares policy iteration

Reference 11

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

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Human-level control through deep reinforcement learning

Reference 12

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

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Multi- task reinforcement learning in reproducing kernel Hilbert spaces via cross- learning

Reference 13

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

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Tensor low-rank approximation of finite- horizon value functions

Reference 14

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This paper cites Lazy approximation for solving continuous finite-horizon MDPs.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Lazy approximation for solving continuous finite-horizon MDPs

Reference 15

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Observation a4c66b62-9844-458d-85d7-3629ac9b26a8 · outbound

This paper cites Finite horizon risk sensitive MDP and linear programming.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Finite horizon risk sensitive MDP and linear programming

Reference 16

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Observation db3b2ccb-d7d9-4dbf-b4b0-bade920361bd · outbound

This paper cites Linear programming formulation for non-stationary, finite-horizon Markov decision process models.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Linear programming formulation for non-stationary, finite-horizon Markov decision process models

Reference 17

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This paper cites A sample-efficient algorithm for episodic finite-horizon MDP with constraints.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation A sample-efficient algorithm for episodic finite-horizon MDP with constraints

Reference 18

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This paper cites Algorithmic survey of parametric value function approximation.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Algorithmic survey of parametric value function approximation

Reference 19

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This paper cites Neural network-based finite-horizon optimal control of uncertain affine nonlinear discrete-time systems.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Neural network-based finite-horizon optimal control of uncertain affine nonlinear discrete-time systems

Reference 20

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This paper cites Neural network-based finite horizon optimal adaptive consensus control of mobile robot formations.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Neural network-based finite horizon optimal adaptive consensus control of mobile robot formations

Reference 21

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This paper cites Deep neural networks algorithms for stochastic control problems on finite horizon: Convergence analysis.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Deep neural networks algorithms for stochastic control problems on finite horizon: Convergence analysis

Reference 22

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This paper cites Sample complexity of episodic fixed-horizon reinforcement learning.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Sample complexity of episodic fixed-horizon reinforcement learning

Reference 23

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This paper cites Fixed-horizon temporal difference methods for stable reinforcement learning.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Fixed-horizon temporal difference methods for stable reinforcement learning

Reference 24

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Tensor decompositions and applications

Reference 25

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Tensor completion and low-n-rank tensor recovery via convex optimization

Reference 26

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Tensor decomposition for signal processing and machine learning

Reference 27

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Low-rank tensor methods for communicating Markov processes

Reference 28

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Low-rank tensor methods for Markov chains with applications to tumor progression models

Reference 29

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Low-Rank Tensors for Multi-Dimensional Markov Models

Reference 30

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This paper cites Reinforcement Learning in Rich-Observation MDPs using Spectral Methods.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Reinforcement Learning in Rich-Observation MDPs using Spectral Methods

Reference 31

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Maximum likelihood tensor decomposition of Markov decision process

Reference 32

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Learning good state and action representations via tensor decomposition

Reference 33

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Learning good state and action representations for Markov decision process via tensor decomposition

Reference 34

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Efficient high- dimensional stochastic optimal motion control using tensor-train decom- position

Reference 35

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation High-dimensional stochas- tic optimal control using continuous tensor decompositions

Reference 36

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Tensor decomposition meth- ods for high-dimensional Hamilton–Jacobi–Bellman equations

Reference 37

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This paper cites Approximating optimal feedback controllers of finite horizon control problems using hierarchical tensor formats.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Approximating optimal feedback controllers of finite horizon control problems using hierarchical tensor formats

Reference 38

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Harnessing structures for value-based planning and reinforcement learning

Reference 39

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Sample efficient reinforcement learning via low-rank matrix estimation

Reference 40

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Low-rank state-action value- function approximation

Reference 41

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Tensor-based reinforcement learning for network routing

Reference 42

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Tensor and matrix low- rank value-function approximation in reinforcement learning

Reference 43

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation PARAFAC. tutorial and applications

Reference 44

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This paper cites Bertsekas, Non-linear programming.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Bertsekas, Non-linear programming

Reference 45

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This paper cites A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factor- ization and completion.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factor- ization and completion

Reference 46

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Revisiting fundamentals of experience replay

Reference 47

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This paper cites A finite time analysis of temporal difference learning with linear function approximation.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation A finite time analysis of temporal difference learning with linear function approximation

Reference 48

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Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation TD conver- gence: An optimization perspective

Reference 49

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Observation a4162db3-6a86-4e06-9fc0-6a47599f5647 · outbound

This paper cites Solving finite-horizon MDPs via tensor low-rank methods.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Solving finite-horizon MDPs via tensor low-rank methods

Reference 50

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Observation 6e8f7713-c812-46d8-8385-6246c68f24a0 · outbound

This paper cites A tutorial on linear function approximators for dynamic programming and reinforcement learning.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation A tutorial on linear function approximators for dynamic programming and reinforcement learning

Reference 51

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Observation c0388454-65c6-4cd8-85a5-3b541eb4880f · outbound

This paper cites Almost-sure iden- tifiability of multidimensional harmonic retrieval.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Almost-sure iden- tifiability of multidimensional harmonic retrieval

Reference 52

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Observation 4f03a075-edec-4f39-a0c6-1fc4ae74a0d6 · outbound

This paper cites Block stochastic gradient iteration for convex and nonconvex optimization.

Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation Block stochastic gradient iteration for convex and nonconvex optimization

Reference 53

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Pith citing papers

Observation 19700877-a78c-4093-bafd-44b6dca8198a · inbound

Unrolling Dynamic Programming via Graph Filters cites this paper.

Unrolling Dynamic Programming via Graph Filters Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation

Reference 30

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