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Summary of Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms Via Adversarial Learning, by Jiashuo Jiang


Constrained Online Two-stage Stochastic Optimization: Near Optimal Algorithms via Adversarial Learning

by Jiashuo Jiang

First submitted to arxiv on: 2 Feb 2023

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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GrooveSquid.com Paper Summaries

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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 explores an online two-stage stochastic optimization problem with long-term constraints. The goal is to minimize the cumulative objective value while ensuring that the average second-stage decision falls within a certain set. To achieve this, the authors develop algorithms based on adversarial learning techniques and show that these algorithms can be adapted to reduce regret bounds. Specifically, when model parameters are drawn from identical distributions, the algorithm achieves an O(sqrt(T)) regret bound, improving previous results under special cases. The algorithm is also robust to adversarial corruptions of model parameter realizations. Additionally, the authors propose a new algorithm for non-stationary distribution settings with a regret bound of O(W_T + sqrt(T)), where W_T measures the total inaccuracy of machine-learned predictions.
Low GrooveSquid.com (original content) Low Difficulty Summary
In simple terms, this paper is about finding the best way to make decisions over time while considering constraints and uncertainty. The authors create new algorithms that can adapt to changing conditions and achieve good results even when some information is incorrect or missing.

Keywords

* Artificial intelligence  * Optimization