Summary of Constrained Online Two-stage Stochastic Optimization: Algorithm with (and Without) Predictions, by Piao Hu et al.
Constrained Online Two-stage Stochastic Optimization: Algorithm with (and without) Predictions
by Piao Hu, Jiashuo Jiang, Guodong Lyu, Hao Su
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative online two-stage stochastic optimization approach for solving complex problems with long-term constraints over a finite horizon. The authors develop algorithms that learn from adversarial learning methods, ensuring regret bounds can be reduced. They demonstrate new results under various settings, including scenarios where model parameters are drawn from unknown non-stationary distributions and machine-learned predictions of these distributions are available. By leveraging these predictions, the proposed algorithm achieves a regret bound of O(W_T + √T), where W_T measures the total inaccuracy of the predictions. The authors also develop an alternative algorithm that performs well without relying on machine-learned predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about solving big problems with rules that change over time. The researchers created new computer algorithms to help us make better decisions when we don’t know exactly how things will turn out. They tested these algorithms in different situations and showed that they work really well, especially when we have some hints about what might happen next. |
Keywords
* Artificial intelligence * Optimization
