Kevin Kögler
3 Publications
2024 | Published | Conference Paper | IST-REx-ID: 17469 | 
K. Kögler, A. Shevchenko, H. Hassani, and M. Mondelli, “Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 24964–25015.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14459 |
A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12540 |
R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally invariant generalized linear models via approximate message passing,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162.
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Grants
3 Publications
2024 | Published | Conference Paper | IST-REx-ID: 17469 |
K. Kögler, A. Shevchenko, H. Hassani, and M. Mondelli, “Compression of structured data with autoencoders: Provable benefit of nonlinearities and depth,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 24964–25015.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14459 |
A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12540 |
R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally invariant generalized linear models via approximate message passing,” in Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, United States, 2022, vol. 162.
[Published Version]
View
| Files available