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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.11848 |
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| _version_ | 1866912669531897856 |
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| author | Perdomo, Juan Carlos Recht, Benjamin |
| author_facet | Perdomo, Juan Carlos Recht, Benjamin |
| contents | This tutorial provides a survey of algorithms for Defensive Forecasting, where predictions are derived not by prognostication but by correcting past mistakes. Pioneered by Vovk, Defensive Forecasting frames the goal of prediction as a sequential game, and derives predictions to minimize metrics no matter what outcomes occur. We present an elementary introduction to this general theory and derive simple, near-optimal algorithms for online learning, calibration, prediction with expert advice, and online conformal prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In Defense of Defensive Forecasting Perdomo, Juan Carlos Recht, Benjamin Machine Learning This tutorial provides a survey of algorithms for Defensive Forecasting, where predictions are derived not by prognostication but by correcting past mistakes. Pioneered by Vovk, Defensive Forecasting frames the goal of prediction as a sequential game, and derives predictions to minimize metrics no matter what outcomes occur. We present an elementary introduction to this general theory and derive simple, near-optimal algorithms for online learning, calibration, prediction with expert advice, and online conformal prediction. |
| title | In Defense of Defensive Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.11848 |