Paper Citation Record · LEDGER

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization

As of 11 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2507.04396.

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

pith.paper-citation-record.v1
2507.04396 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:58:57.173888Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

23 of 23 outbound references displayed

verified exact1
verified fuzzy14
unresolved5
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External citation measurements

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Outbound references

Observation 7b56e6eb-7c03-48ca-a495-2d65b8ae96e7 · outbound

This paper cites The construction of utility functions from expenditure data.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization The construction of utility functions from expenditure data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:59.723242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 20a30a65-8223-46c7-b36f-0cb97649882d · outbound

This paper cites Finite-sample bounds for adaptive inverse reinforcement learn- ing using passive langevin dynamics.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Finite-sample bounds for adaptive inverse reinforcement learn- ing using passive langevin dynamics

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:58.304324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e4fa3006-cc10-42b7-9cf6-b5b4e3e08c9f · outbound

This paper cites The strong ergodic theorem for densities: generalized Shannon-McMillan- Breiman theorem.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization The strong ergodic theorem for densities: generalized Shannon-McMillan- Breiman theorem

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:59.340275Z

Source-reported events for the cited work

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Observation 4bb9b298-080e-455e-9c0c-ed38d1f1b1b8 · outbound

This paper cites Fine-Tuning Language Models from Human Preferences.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Fine-Tuning Language Models from Human Preferences

Reference 8

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Source-reported events for the cited work

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Observation 647bfaa8-b775-4bec-963c-d2dac5be68ca · outbound

This paper cites Unifying Revealed Preference and Revealed Rational Inattention.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Unifying Revealed Preference and Revealed Rational Inattention

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 95b75689-967f-4dd6-9368-cff862584bfe · outbound

This paper cites Continuous Inverse Optimal Control with Locally Optimal Examples.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Continuous Inverse Optimal Control with Locally Optimal Examples

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:56.195785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5386775-11de-49d5-ad02-a64e1a954616 · outbound

This paper cites Langevin-type models I: diffusions with given stationary distri- butions and their discretizations.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Langevin-type models I: diffusions with given stationary distri- butions and their discretizations

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:58.104986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 32b137b8-f760-406d-919e-53a95a647e76 · outbound

This paper cites Regularized Inverse Reinforcement Learning.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Regularized Inverse Reinforcement Learning

Reference 500

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T19:58:57.919764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2569f676-3f3e-4db9-b200-5f4730b25031 · outbound

This paper cites Apprenticeship learning via inverse reinforcement learning.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Apprenticeship learning via inverse reinforcement learning

Reference 1979

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:59.513129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bf50c81a-a70c-4880-8b55-b709b10d04b9 · outbound

This paper cites Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis

Reference 1983

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:56.687880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ee527b1-8e4e-4cef-b3b4-bdec461142b5 · outbound

This paper cites Real-Time Reinforcement Learning of Constrained Markov Decision Processes with Weak Derivatives.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Real-Time Reinforcement Learning of Constrained Markov Decision Processes with Weak Derivatives

Reference 1984

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:58:57.731086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c55b22dd-4e31-4762-95a5-666c22ad807c · outbound

This paper cites Learning Robust Rewards with Adversarial Inverse Reinforcement Learning.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

Reference 1986

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:55.741770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c777eb7-1aa4-4dae-b8cb-49b465d09ccf · outbound

This paper cites Thompson sampling for contextual bandits with linear payoffs.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Thompson sampling for contextual bandits with linear payoffs

Reference 1987

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:59.616636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6cb46f9-24b5-46c4-9102-7ddb9239e337 · outbound

This paper cites Maximum margin planning.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Maximum margin planning

Reference 1994

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:58.660502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 37034726-4831-418d-942c-ad481c0e2582 · outbound

This paper cites Identifiability in inverse reinforcement learning.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Identifiability in inverse reinforcement learning

Reference 1996

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:59.115812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8d32fc73-a7b3-4f8e-9a60-4926d3e02098 · outbound

This paper cites Risk-constrained markov decision processes.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Risk-constrained markov decision processes

Reference 1999

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:59.238305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 33cd53aa-83c8-4483-82c3-9379582fd007 · outbound

This paper cites Maximum Entropy Deep Inverse Reinforcement Learning.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Maximum Entropy Deep Inverse Reinforcement Learning

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:57.101600Z

Source-reported events for the cited work

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Observation 0e353394-f97b-4048-908a-7f3847e7917b · outbound

This paper cites Langevin dynamics for adaptive inverse reinforcement learning of stochastic gradient algorithms.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Langevin dynamics for adaptive inverse reinforcement learning of stochastic gradient algorithms

Reference 2003

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:58.787186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8d787def-222d-47e9-8df7-e8a05bcab7dd · outbound

This paper cites A testable model of consumption with externalities.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization A testable model of consumption with externalities

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:58.988265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1dc32662-005d-4093-b93a-1ef086834415 · outbound

This paper cites A characterization of rationalizable consumer behavior.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization A characterization of rationalizable consumer behavior

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:58.549024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 34e537b0-045d-46d9-bc69-c994cef7dd9c · outbound

This paper cites Apprenticeship Learning for Model Parameters of Partially Observable Environments.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Apprenticeship Learning for Model Parameters of Partially Observable Environments

Reference 2016

Resolution: malformed identifier; local_arxiv, observed 2026-08-06T19:58:57.566558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0eb391e-3ab3-4faf-83cf-738ee6a8f88b · outbound

This paper cites Implications of rational inattention.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Implications of rational inattention

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:58.416044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation aa3ce0cc-7668-4d19-8340-b4d61fb6912b · outbound

This paper cites Inverse game theory: learning utilities in succinct games.

Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization Inverse game theory: learning utilities in succinct games

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:58.883483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.