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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.16875 |
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| _version_ | 1866917503099207680 |
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| author | Avetisyan, Arutyun Dvinskikh, Darina Gasnikov, Alexander Temlyakov, Vladimir Tupitsa, Nazarii Turdakov, Denis |
| author_facet | Avetisyan, Arutyun Dvinskikh, Darina Gasnikov, Alexander Temlyakov, Vladimir Tupitsa, Nazarii Turdakov, Denis |
| contents | This paper aims to motivate stochastic optimization problems from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood or minimize the population risk. We briefly describe the two main approaches: offline (Monte Carlo / Sample Average Approximation) and online (Stochastic Approximation) approaches -- to solve the expectation minimization problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16875 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stochastic Optimization and Data Science Avetisyan, Arutyun Dvinskikh, Darina Gasnikov, Alexander Temlyakov, Vladimir Tupitsa, Nazarii Turdakov, Denis Optimization and Control This paper aims to motivate stochastic optimization problems from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood or minimize the population risk. We briefly describe the two main approaches: offline (Monte Carlo / Sample Average Approximation) and online (Stochastic Approximation) approaches -- to solve the expectation minimization problems. |
| title | Stochastic Optimization and Data Science |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2605.16875 |