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77 Publications
2025 | Published | Journal Article | IST-REx-ID: 18986 | 
Barbier, Jean, Francesco Camilli, Yizhou Xu, and Marco Mondelli. “Information Limits and Thouless-Anderson-Palmer Equations for Spiked Matrix Models with Structured Noise.” Physical Review Research. American Physical Society, 2025. https://doi.org/10.1103/PhysRevResearch.7.013081.
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| arXiv
2025 | Epub ahead of print | Journal Article | IST-REx-ID: 20081 |
Esposito, Amedeo Roberto, Michael Gastpar, and Ibrahim Issa. “Sibson α-Mutual Information and Its Variational Representations.” IEEE Transactions on Information Theory. Institute of Electrical and Electronics Engineers, 2025. https://doi.org/10.1109/TIT.2025.3587340.
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2025 | Published | Conference Paper | IST-REx-ID: 20033 |
Emrullah Ildiz, M., Halil Alperen Gozeten, Ege Onur Taga, Marco Mondelli, and Samet Oymak. “High-Dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws.” In 13th International Conference on Learning Representations, 2967–3006. ICLR, 2025.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20035 |
Jacot, Arthur, Peter Súkeník, Zihan Wang, and Marco Mondelli. “Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse.” In 13th International Conference on Learning Representations, 1905–31. ICLR, 2025.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20300 |
Wegel, Tobias, Filip Kovačević, Alexandru Ţifrea, and Fanny Yang. “Learning Pareto Manifolds in High Dimensions: How Can Regularization Help?” In The 28th International Conference on Artificial Intelligence and Statistics, 258:4591–99. ML Research Press, 2025.
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| arXiv
2025 | Published | Journal Article | IST-REx-ID: 19065 |
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Fornasier, Massimo, Timo Klock, Marco Mondelli, and Michael Rauchensteiner. “Efficient Identification of Wide Shallow Neural Networks with Biases.” Applied and Computational Harmonic Analysis. Elsevier, 2025. https://doi.org/10.1016/j.acha.2025.101749.
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| WoS
2025 | Published | Conference Paper | IST-REx-ID: 19281 |
Resch, Nicolas, Chen Yuan, and Yihan Zhang. “Tight Bounds on List-Decodable and List-Recoverable Zero-Rate Codes.” In 16th Innovations in Theoretical Computer Science Conference, Vol. 325. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2025. https://doi.org/10.4230/LIPIcs.ITCS.2025.82.
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| arXiv
2025 | Published | Journal Article | IST-REx-ID: 19627 |
Bombari, Simone, and Marco Mondelli. “Privacy for Free in the Overparameterized Regime.” Proceedings of the National Academy of Sciences. National Academy of Sciences, 2025. https://doi.org/10.1073/pnas.2423072122.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20667
El Latif Kadry, Abd, Yihan Zhang, and Nir Weinberger. “Mean Estimation in High-Dimensional Binary Timeinhomogeneous Markov Gaussian Mixture Models.” In 2025 IEEE International Symposium on Information Theory Proceedings. IEEE, 2025. https://doi.org/10.1109/ISIT63088.2025.11195426.
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2024 | Published | Journal Article | IST-REx-ID: 18652
Dey, Bikash Kumar, Sidharth Jaggi, Michael Langberg, Anand D. Sarwate, and Yihan Zhang. “Codes for Adversaries: Between Worst-Case and Average-Case Jamming.” Foundations and Trends in Communications and Information Theory. Now Publishers, 2024. https://doi.org/10.1561/0100000112.
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2024 | Published | Conference Paper | IST-REx-ID: 18973 |
Bombari, Simone, and Marco Mondelli. “Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features.” In 41st International Conference on Machine Learning, 235:4300–4328. ML Research Press, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18972 |
Bombari, Simone, and Marco Mondelli. “How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features.” In 41st International Conference on Machine Learning, 235:4267–99. ML Research Press, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18897 |
Pedrotti, Francesco, Jan Maas, and Marco Mondelli. “Improved Convergence of Score-Based Diffusion Models via Prediction-Correction.” In Transactions on Machine Learning Research, 2024.
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| arXiv
2024 | Draft | Preprint | IST-REx-ID: 17350 |
Pedrotti, Francesco, Jan Maas, and Marco Mondelli. “Improved Convergence of Score-Based Diffusion Models via Prediction-Correction.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2305.14164.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18890 |
Beaglehole, Daniel, Peter Súkeník, Marco Mondelli, and Mikhail Belkin. “Average Gradient Outer Product as a Mechanism for Deep Neural Collapse.” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 19518 |
Wu, Diyuan, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, and Dan-Adrian Alistarh. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18891 |
Súkeník, Peter, Christoph Lampert, and Marco Mondelli. “Neural Collapse versus Low-Rank Bias: Is Deep Neural Collapse Really Optimal?” In 38th Annual Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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| arXiv
2024 | Published | Journal Article | IST-REx-ID: 14665 |
Zhang, Yihan, and Shashank Vatedka. “Multiple Packing: Lower Bounds via Error Exponents.” IEEE Transactions on Information Theory. IEEE, 2024. https://doi.org/10.1109/TIT.2023.3334032.
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| arXiv
2024 | Published | Journal Article | IST-REx-ID: 15172 |
Esposito, Amedeo Roberto, and Marco Mondelli. “Concentration without Independence via Information Measures.” IEEE Transactions on Information Theory. IEEE, 2024. https://doi.org/10.1109/TIT.2024.3367767.
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| arXiv
2024 | Published | Journal Article | IST-REx-ID: 17330 |
Resch, Nicolas, Chen Yuan, and Yihan Zhang. “Zero-Rate Thresholds and New Capacity Bounds for List-Decoding and List-Recovery.” IEEE Transactions on Information Theory. IEEE, 2024. https://doi.org/10.1109/TIT.2024.3430842.
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