Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:21:46.594299Z
Paper Citation Record · LEDGER
As of 15 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2608.13554.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T04:21:46.594299Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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
73 of 73 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 6847fb91-f095-4835-bec5-e4d4281af2c1 · outbound
Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 36th Annual IEEE Symposium on Foundations of Computer Science , pages =
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Defensive Boosting for Online Probabilistic Forecasting Advances in Neural Information Processing Systems , volume =
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Defensive Boosting for Online Probabilistic Forecasting A Pseudorandom Generator from Any One-Way Function , journal =
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Defensive Boosting for Online Probabilistic Forecasting , title =
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Defensive Boosting for Online Probabilistic Forecasting , title =
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Defensive Boosting for Online Probabilistic Forecasting , title =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of Innovations in Computer Science , pages =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 32nd International Conference on Machine Learning , pages =
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Defensive Boosting for Online Probabilistic Forecasting Advances in Neural Information Processing Systems 28 , pages =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 20th International Conference on Artificial Intelligence and Statistics , pages =
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Defensive Boosting for Online Probabilistic Forecasting Advances in Neural Information Processing Systems 33 , pages =
Reference 18
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Defensive Boosting for Online Probabilistic Forecasting Advances in Neural Information Processing Systems , volume =
Reference 19
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Defensive Boosting for Online Probabilistic Forecasting Revisiting Agnostic Boosting , booktitle =
Reference 20
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Defensive Boosting for Online Probabilistic Forecasting Sample-Near-Optimal Agnostic Boosting with Improved Running Time , booktitle =
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Defensive Boosting for Online Probabilistic Forecasting and Ghorbani, Amirata and Zou, James , title =
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Defensive Boosting for Online Probabilistic Forecasting and Reingold, Omer and Rothblum, Guy N
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 40th International Conference on Machine Learning , pages =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 40th International Conference on Machine Learning , pages =
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Observation 2b4e33f8-a4c9-4690-8535-83c005f7ee49 · outbound
Defensive Boosting for Online Probabilistic Forecasting Complexity-Theoretic Implications of Multicalibration , booktitle =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms , pages =
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Defensive Boosting for Online Probabilistic Forecasting How Global Calibration Strengthens Multiaccuracy , booktitle =
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Defensive Boosting for Online Probabilistic Forecasting arXiv preprint arXiv:2507.09683 , year =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing , pages =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 57th Annual ACM Symposium on Theory of Computing , pages =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the 42nd International Conference on Machine Learning , pages =
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Defensive Boosting for Online Probabilistic Forecasting Optimal Lower Bounds for Online Multicalibration
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Defensive Boosting for Online Probabilistic Forecasting An Efficient Black-Box Reduction from Online Learning to Multicalibration, and a New Route to $\Phi$-Regret Minimization
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Observation 42500648-d7d9-4e3e-b57f-ba934dcfa737 · outbound
Defensive Boosting for Online Probabilistic Forecasting Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics , pages =
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Defensive Boosting for Online Probabilistic Forecasting , title =
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Defensive Boosting for Online Probabilistic Forecasting Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics , pages =
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Defensive Boosting for Online Probabilistic Forecasting Algorithmic Learning Theory, 16th International Conference, ALT 2005 , series =
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Reference 66
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Reference 70
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Reference 71
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Observation 194b05a5-0c45-4d96-b392-91efc8a79335 · outbound
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Reference 73
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No inbound Pith citation observations are available.