Alexander Shevchenko

Graduate School
Mondelli Group

5 Publications

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[5]
2024 | Published | Thesis | IST-REx-ID: 17465 | OA
Shevchenko A. 2024. High-dimensional limits in artificial neural networks. Institute of Science and Technology Austria.
[Published Version] View | Files available | DOI
 
[4]
2024 | Published | Conference Paper | IST-REx-ID: 17469 | OA
Kögler K, Shevchenko A, Hassani H, Mondelli M. 2024. Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 24964–25015.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[3]
2023 | Published | Conference Paper | IST-REx-ID: 14459 | OA
Shevchenko A, Kögler K, Hassani H, Mondelli M. 2023. Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 31151–31209.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2022 | Published | Journal Article | IST-REx-ID: 11420 | OA
Shevchenko A, Kungurtsev V, Mondelli M. 2022. Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. 23(130), 1–55.
[Published Version] View | Files available | arXiv
 
[1]
2020 | Published | Conference Paper | IST-REx-ID: 9198 | OA
Shevchenko A, Mondelli M. 2020. Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. Proceedings of the 37th International Conference on Machine Learning. vol. 119, 8773–8784.
[Published Version] View | Files available | arXiv
 
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5 Publications

Mark all

[5]
2024 | Published | Thesis | IST-REx-ID: 17465 | OA
Shevchenko A. 2024. High-dimensional limits in artificial neural networks. Institute of Science and Technology Austria.
[Published Version] View | Files available | DOI
 
[4]
2024 | Published | Conference Paper | IST-REx-ID: 17469 | OA
Kögler K, Shevchenko A, Hassani H, Mondelli M. 2024. Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 24964–25015.
[Published Version] View | Files available | Download Published Version (ext.) | arXiv
 
[3]
2023 | Published | Conference Paper | IST-REx-ID: 14459 | OA
Shevchenko A, Kögler K, Hassani H, Mondelli M. 2023. Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 31151–31209.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[2]
2022 | Published | Journal Article | IST-REx-ID: 11420 | OA
Shevchenko A, Kungurtsev V, Mondelli M. 2022. Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. 23(130), 1–55.
[Published Version] View | Files available | arXiv
 
[1]
2020 | Published | Conference Paper | IST-REx-ID: 9198 | OA
Shevchenko A, Mondelli M. 2020. Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. Proceedings of the 37th International Conference on Machine Learning. vol. 119, 8773–8784.
[Published Version] View | Files available | arXiv
 
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Citation Style: ISTA Annual Report

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