Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T16:35:03.105674Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 0 inbound Pith citation observations for arXiv:2607.17981.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-01T16:35:03.105674Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
67 of 67 outbound references displayed
External citation measurements
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Observation 8b973569-0a9d-4645-a0ad-adfc14c6d9b5 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning 135 , author=
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Information-Based Exploration via Random Features for Reinforcement Learning 2009 , publisher=
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Observation ccee3aef-b1a1-47b1-b373-17ec9e754585 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning Journal of Computer and System Sciences , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning Machine learning , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning Proceedings of the 26th annual international conference on machine learning , pages=
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , pages=
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Information-Based Exploration via Random Features for Reinforcement Learning International conference on machine learning , pages=
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Information-Based Exploration via Random Features for Reinforcement Learning Never Give Up: Learning Directed Exploration Strategies
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Information-Based Exploration via Random Features for Reinforcement Learning Exploration by Random Network Distillation
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Information-Based Exploration via Random Features for Reinforcement Learning Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
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Information-Based Exploration via Random Features for Reinforcement Learning International Conference on Artificial Intelligence and Statistics , pages=
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Information-Based Exploration via Random Features for Reinforcement Learning Finite-Time Analysis of Kernelised Contextual Bandits
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning Biometrika , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning , author=
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Observation 0a5a3956-6f64-4731-9908-85a01827412e · outbound
Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning 2018 Information Theory and Applications Workshop (ITA) , pages=
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Observation 3ca72a6e-3175-47f2-b45a-2fe9f1f9a849 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning International conference on machine learning , pages=
Reference 21
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Observation 6d0d0be8-2b84-40f4-a650-31a938adcc53 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning
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Information-Based Exploration via Random Features for Reinforcement Learning Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
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Information-Based Exploration via Random Features for Reinforcement Learning Bayesian
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Information-Based Exploration via Random Features for Reinforcement Learning International conference on computational learning theory , pages=
Reference 26
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Observation 84e5d018-740b-43e3-98ba-8a47a1c10756 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning Transactions of the American mathematical society , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning 1999 , publisher=
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Information-Based Exploration via Random Features for Reinforcement Learning Unresolved cited work
Reference 29
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Observation 663c2707-9b88-4f2c-91e1-4a438be9f0d2 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning Seventh International Workshop on Artificial Intelligence and Statistics , year=
Reference 30
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning 1950 , publisher=
Reference 32
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Observation 947f7b1f-c05f-4a22-876a-9f4c4ac29e27 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning On the Error of Random Fourier Features
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Observation b2e4d548-fd34-43c7-811d-c4f40a992433 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning Vanilla Bayesian Optimization Performs Great in High Dimensions
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Observation 2371d824-98b6-48bf-bab1-cadb347adf0d · outbound
Information-Based Exploration via Random Features for Reinforcement Learning ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift f
Reference 36
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Observation 08276c68-31e2-4c9c-8b9d-b8049b0d8df7 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning
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Observation 0ff04bd2-f22d-4e85-9154-f568064ea6b0 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning Proximal Policy Optimization Algorithms
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Observation 1d81ace4-312e-4756-aad4-21fe334b4918 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning Daniel Freeman and Erik Frey and Anton Raichuk and Sertan Girgin and Igor Mordatch and Olivier Bachem , title =
Reference 39
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Information-Based Exploration via Random Features for Reinforcement Learning Unresolved cited work
Reference 40
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Observation 3bdfbfa2-0fdf-4c3a-af13-3aeaf461fd07 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning NAVIX: Scaling MiniGrid Environments with JAX
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Observation bc458cce-5a2c-491f-8fa9-397615687046 · outbound
Information-Based Exploration via Random Features for Reinforcement Learning OGBench: Benchmarking Offline Goal-Conditioned RL
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Information-Based Exploration via Random Features for Reinforcement Learning 1992 , publisher=
Reference 43
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Information-Based Exploration via Random Features for Reinforcement Learning Unresolved cited work
Reference 44
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Information-Based Exploration via Random Features for Reinforcement Learning Fast active learning for pure exploration in reinforcement learning
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Information-Based Exploration via Random Features for Reinforcement Learning 1959 , publisher=
Reference 46
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Information-Based Exploration via Random Features for Reinforcement Learning MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization
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Information-Based Exploration via Random Features for Reinforcement Learning Proceedings of the thiry-fourth annual ACM symposium on Theory of computing , pages=
Reference 48
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Information-Based Exploration via Random Features for Reinforcement Learning Communications of the ACM , volume=
Reference 49
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in neural information processing systems , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning Proceedings of the thirteenth international conference on artificial intelligence and statistics , pages=
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Information-Based Exploration via Random Features for Reinforcement Learning 2013 IEEE international conference on acoustics, speech and signal processing , pages=
Reference 53
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Information-Based Exploration via Random Features for Reinforcement Learning Proceedings of the AAAI conference on artificial intelligence , volume=
Reference 54
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Information-Based Exploration via Random Features for Reinforcement Learning Advances in Neural Information Processing Systems , volume=
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Information-Based Exploration via Random Features for Reinforcement Learning The Thirteenth International Conference on Learning Representations , year=
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Information-Based Exploration via Random Features for Reinforcement Learning Unresolved cited work
Reference 60
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Information-Based Exploration via Random Features for Reinforcement Learning Unresolved cited work
Reference 62
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Observation 7f77f602-e6d3-42a7-b63c-ec34e0b31f80 · outbound
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Information-Based Exploration via Random Features for Reinforcement Learning The 22nd International Conference on Artificial Intelligence and Statistics , pages=
Reference 64
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Information-Based Exploration via Random Features for Reinforcement Learning Kernel-Based Reinforcement Learning in Robust
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Information-Based Exploration via Random Features for Reinforcement Learning International Conference on Artificial Intelligence and Statistics , pages=
Reference 66
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Information-Based Exploration via Random Features for Reinforcement Learning Cognitive Neuroscience , volume=
Reference 67
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