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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2512.01258 |
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| _version_ | 1866909937478664192 |
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| author | Isik, Berivan Fahrbach, Matthew Kuzmin, Dima Mayoraz, Nicolas Praun, Emil Rendle, Steffen Vasudeva, Raghavendra |
| author_facet | Isik, Berivan Fahrbach, Matthew Kuzmin, Dima Mayoraz, Nicolas Praun, Emil Rendle, Steffen Vasudeva, Raghavendra |
| contents | Online learning is the cornerstone of applications like recommendation and advertising systems, where models continuously adapt to shifting data distributions. Model training for such systems is remarkably expensive, a cost that multiplies during hyperparameter search. We introduce a two-stage paradigm to reduce this cost: (1) efficiently identifying the most promising configurations, and then (2) training only these selected candidates to their full potential. Our core insight is that focusing on accurate identification in the first stage, rather than achieving peak performance, allows for aggressive cost-saving measures. We develop novel data reduction and prediction strategies that specifically overcome the challenges of sequential, non-stationary data not addressed by conventional hyperparameter optimization. We validate our framework's effectiveness through a dual evaluation: first on the Criteo 1TB dataset, the largest suitable public benchmark, and second on an industrial advertising system operating at a scale two orders of magnitude larger. Our methods reduce the total hyperparameter search cost by up to 10$\times$ on the public benchmark and deliver significant, validated efficiency gains in the industrial setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01258 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Hyperparameter Search for Non-Stationary Model Training Isik, Berivan Fahrbach, Matthew Kuzmin, Dima Mayoraz, Nicolas Praun, Emil Rendle, Steffen Vasudeva, Raghavendra Machine Learning Online learning is the cornerstone of applications like recommendation and advertising systems, where models continuously adapt to shifting data distributions. Model training for such systems is remarkably expensive, a cost that multiplies during hyperparameter search. We introduce a two-stage paradigm to reduce this cost: (1) efficiently identifying the most promising configurations, and then (2) training only these selected candidates to their full potential. Our core insight is that focusing on accurate identification in the first stage, rather than achieving peak performance, allows for aggressive cost-saving measures. We develop novel data reduction and prediction strategies that specifically overcome the challenges of sequential, non-stationary data not addressed by conventional hyperparameter optimization. We validate our framework's effectiveness through a dual evaluation: first on the Criteo 1TB dataset, the largest suitable public benchmark, and second on an industrial advertising system operating at a scale two orders of magnitude larger. Our methods reduce the total hyperparameter search cost by up to 10$\times$ on the public benchmark and deliver significant, validated efficiency gains in the industrial setting. |
| title | Efficient Hyperparameter Search for Non-Stationary Model Training |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2512.01258 |