Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2404.03578.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:45:55.531036Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T09:12:14.617137Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation c237809b-e432-4ccc-8c39-684560ff0331 · inbound
Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis cites this paper.
Model-Free Robust Average-Reward Reinforcement Learning with Sample Complexity Analysis Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
Reference 91
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:45:55.531036Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 84266ef8-f340-4834-b8df-54d1db93a83d · inbound
Causality-Inspired Robustness for Nonlinear Models via Representation Learning cites this paper.
Causality-Inspired Robustness for Nonlinear Models via Representation Learning Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
Reference 2017
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:36:34.373712Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7fc2d69d-d1b2-41ba-8a55-fa137634eabd · inbound
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning cites this paper.
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:37:25.885881Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 01179285-566e-4a16-bf58-3d2eedf68f32 · inbound
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty cites this paper.
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-07-14T01:21:58.131331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 08c2a4d9-6e1b-4f28-8799-f4f598767489 · inbound
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation cites this paper.
Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-07-14T01:21:58.131331Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 01267869-c504-4f99-a017-92ae53995928 · inbound
Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions cites this paper.
Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithms
Reference 165
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T14:39:15.846221Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.