Distributionally Robust Optimization with Markovian Data

Li, Mengmeng; Sutter, Tobias; Kuhn, Daniel

Distributionally Robust Optimization with Markovian Data

Dateien

Li_2-tkazjz82mggz2.PDFGröße: 553.68 KBDownloads: 6

Datum

2021

Autor:innen

Li, Mengmeng

Sutter, Tobias

Kuhn, Daniel

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

MEILA, Marina, ed., Tong ZHANG, ed.. Proceedings of the 38th International Conference on Machine Learning. 2021, pp. 6493-6503. Proceedings of Machine Learning Research. 139. ISSN 2640-3498

Zusammenfassung

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven distributionally robust optimization model to estimate the problem’s objective function and optimal solution. By leveraging results from large deviations theory, we derive statistical guarantees on the quality of these estimators. The underlying worst-case expectation problem is nonconvex and involves O(d²) decision variables. Thus, it cannot be solved efficiently for large d. By exploiting the structure of this problem, we devise a customized Frank-Wolfe algorithm with convex direction-finding subproblems of size O(d). We prove that this algorithm finds a stationary point efficiently under mild conditions. The efficiency of the method is predicated on a dimensionality reduction enabled by a dual reformulation. Numerical experiments indicate that our approach has better computational and statistical properties than the state-of-the-art methods.

Fachgebiet (DDC)

004 Informatik

Konferenz

38th International Conference on Machine Learning (Virtual), 18. Juli 2021 - 24. Juli 2021

Zitieren

ISO 690

LI, Mengmeng, Tobias SUTTER, Daniel KUHN, 2021. Distributionally Robust Optimization with Markovian Data. 38th International Conference on Machine Learning (Virtual), 18. Juli 2021 - 24. Juli 2021. In: MEILA, Marina, ed., Tong ZHANG, ed.. Proceedings of the 38th International Conference on Machine Learning. 2021, pp. 6493-6503. Proceedings of Machine Learning Research. 139. ISSN 2640-3498

BibTex

@inproceedings{Li2021Distr-55737,
  year={2021},
  title={Distributionally Robust Optimization with Markovian Data},
  url={https://proceedings.mlr.press/v139/li21t.html},
  number={139},
  issn={2640-3498},
  series={Proceedings of Machine Learning Research},
  booktitle={Proceedings of the 38th International Conference on Machine Learning},
  pages={6493--6503},
  editor={Meila, Marina and Zhang, Tong},
  author={Li, Mengmeng and Sutter, Tobias and Kuhn, Daniel}
}

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URL der Originalveröffentl.

https://proceedings.mlr.press/v139/li21t.html

Prüfdatum der URL

2021-12-02

Universitätsbibliographie

Ja