Kevin Kögler
3 Publications
2024 | Published | Conference Paper | IST-REx-ID: 17469 | 
Kögler, Kevin, Alexander Shevchenko, Hamed Hassani, and Marco Mondelli. “Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth.” In Proceedings of the 41st International Conference on Machine Learning, 235:24964–15. ML Research Press, 2024.
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| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14459 |
Shevchenko, Alexander, Kevin Kögler, Hamed Hassani, and Marco Mondelli. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” In Proceedings of the 40th International Conference on Machine Learning, 202:31151–209. ML Research Press, 2023.
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| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12540 |
Venkataramanan, Ramji, Kevin Kögler, and Marco Mondelli. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” In Proceedings of the 39th International Conference on Machine Learning, Vol. 162. ML Research Press, 2022.
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3 Publications
2024 | Published | Conference Paper | IST-REx-ID: 17469 |
Kögler, Kevin, Alexander Shevchenko, Hamed Hassani, and Marco Mondelli. “Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth.” In Proceedings of the 41st International Conference on Machine Learning, 235:24964–15. ML Research Press, 2024.
[Published Version]
View
| Files available
| Download Published Version (ext.)
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14459 |
Shevchenko, Alexander, Kevin Kögler, Hamed Hassani, and Marco Mondelli. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” In Proceedings of the 40th International Conference on Machine Learning, 202:31151–209. ML Research Press, 2023.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12540 |
Venkataramanan, Ramji, Kevin Kögler, and Marco Mondelli. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” In Proceedings of the 39th International Conference on Machine Learning, Vol. 162. ML Research Press, 2022.
[Published Version]
View
| Files available