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Marco Mondelli

Marek Jankola

Simone Bombari

Al Depope

Yijun Dong

Kevin Kögler

Filip Kovacevic

Cristina Lopez Amado

Vova Masalitin

Pablo Pascual Cobo

Peter Sukenik

Diyuan Wu

Edwige Cyffers

Clementine Domine

Amedeo Esposito

Aleksandr Shevchenko

Yihan Zhang



88 Publications

2026 | Published | Journal Article | IST-REx-ID: 20081 | OA
A. R. Esposito, M. Gastpar, and I. Issa, “Sibson α-mutual information and its variational representations,” IEEE Transactions on Information Theory, vol. 72, no. 7. IEEE, pp. 4434–4467, 2026.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2026 | Published | Thesis | PhD | IST-REx-ID: 22258 | OA
A. Depope, “From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics,” Institute of Science and Technology Austria, 2026.
[Published Version] View | Files available | DOI
 
2026 | Published | Journal Article | IST-REx-ID: 21488 | OA
A. Depope, J. Bajzik, M. Mondelli, and M. R. Robinson, “Joint modeling of whole-genome sequencing data for human height via approximate message passing,” Cell Genomics, vol. 6, no. 5. Elsevier, 2026.
[Published Version] View | Files available | DOI | PubMed | Europe PMC
 
2026 | Published | Journal Article | IST-REx-ID: 22228 | OA | PlanS
Y. Zhang, M. Mondelli, and R. Venkataramanan, “Precise asymptotics for spectral methods in mixed generalized linear models,” SIAM Journal on Mathematics of Data Science, vol. 8, no. 2. SIAM, pp. 411–439, 2026.
[Published Version] View | Files available | DOI | arXiv
 
2026 | Published | Thesis | PhD | IST-REx-ID: 22857 | OA
S. Bombari, “Trustworthy machine learning in high dimensions,” Institute of Science and Technology Austria, 2026.
[Published Version] View | Files available | DOI
 
2026 | Published | Conference Paper | IST-REx-ID: 22894 | OA
L. Iurada, S. Bombari, T. Tommasi, and M. Mondelli, “A law of data reconstruction for random features (and beyond),” in 14th International Conference on Learning Representations, Rio de Janeiro, Brazil, 2026, vol. 2026, pp. 145275–145314.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 21325 | OA
H. A. Gozeten, M. E. Ildiz, X. Zhang, M. Soltanolkotabi, M. Mondelli, and S. Oymak, “Test-time training provably improves transformers as in-context learners,” in Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada, 2025, vol. 267, pp. 20266–20295.
[Published Version] View | Files available | PubMed | Europe PMC
 
2025 | Published | Conference Paper | IST-REx-ID: 21328 | OA
F. Kovačević, Z. Yihan, and M. Mondelli, “Spectral estimators for multi-index models: Precise asymptotics and optimal weak recovery,” in Proceedings of 38th Conference on Learning Theory, Lyon, France, 2025, vol. 291, pp. 3354–3404.
[Published Version] View | Files available | arXiv
 
2025 | Published | Journal Article | IST-REx-ID: 18986 | OA
J. Barbier, F. Camilli, Y. Xu, and M. Mondelli, “Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise,” Physical Review Research, vol. 7. American Physical Society, 2025.
[Published Version] View | Files available | DOI | arXiv
 
2025 | Published | Journal Article | IST-REx-ID: 19065 | OA | PlanS
M. Fornasier, T. Klock, M. Mondelli, and M. Rauchensteiner, “Efficient identification of wide shallow neural networks with biases,” Applied and Computational Harmonic Analysis, vol. 77. Elsevier, 2025.
[Published Version] View | Files available | DOI | WoS
 
2025 | Published | Conference Paper | IST-REx-ID: 19281 | OA
N. Resch, C. Yuan, and Y. Zhang, “Tight bounds on list-decodable and list-recoverable zero-rate codes,” in 16th Innovations in Theoretical Computer Science Conference, New York, NY, United States, 2025, vol. 325.
[Published Version] View | Files available | DOI | WoS | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20033 | OA
M. Emrullah Ildiz, H. A. Gozeten, E. O. Taga, M. Mondelli, and S. Oymak, “High-dimensional analysis of knowledge distillation: Weak-to-Strong generalization and scaling laws,” in 13th International Conference on Learning Representations, Singapore, Singapore, 2025, pp. 2967–3006.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20035 | OA
A. Jacot, P. Súkeník, Z. Wang, and M. Mondelli, “Wide neural networks trained with weight decay provably exhibit neural collapse,” in 13th International Conference on Learning Representations, Singapore, Singapore, 2025, pp. 1905–1931.
[Published Version] View | Files available | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20300 | OA
T. Wegel, F. Kovačević, A. Ţifrea, and F. Yang, “Learning Pareto manifolds in high dimensions: How can regularization help?,” in The 28th International Conference on Artificial Intelligence and Statistics, Mai Khao, Thailand, 2025, vol. 258, pp. 4591–4599.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 20667
A. El Latif Kadry, Y. Zhang, and N. Weinberger, “Mean estimation in high-dimensional binary timeinhomogeneous Markov Gaussian mixture models,” in 2025 IEEE International Symposium on Information Theory Proceedings, Ann Arbor, MI, United States, 2025.
View | DOI
 
2025 | Published | Journal Article | IST-REx-ID: 20734 | OA | PlanS
Y. Zhang, H. C. Ji, R. Venkataramanan, and M. Mondelli, “Spectral estimators for structured generalized linear models via approximate message passing,” Mathematical Statistics and Learning, vol. 8, no. 3–4. EMS Press, pp. 193–304, 2025.
[Published Version] View | Files available | DOI
 
2025 | Published | Conference Paper | IST-REx-ID: 22825 | OA
P. Súkeník, C. Lampert, and M. Mondelli, “Neural collapse is globally optimal in deep regularized ResNets and transformers,” in 39th Conference on Neural Information Processing Systems, San Diego, CA, United States, 2025, vol. 38, pp. 48646–48677.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 21326 | OA
D. Wu and M. Mondelli, “Neural collapse beyond the unconstrained features model: Landscape, dynamics, and generalization in the mean-field regime,” in Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada, 2025, vol. 267, pp. 67499–67536.
[Published Version] View | Files available | arXiv
 
2025 | Published | Journal Article | IST-REx-ID: 19627 | OA
S. Bombari and M. Mondelli, “Privacy for free in the overparameterized regime,” Proceedings of the National Academy of Sciences, vol. 122, no. 15. National Academy of Sciences, 2025.
[Published Version] View | Files available | DOI | WoS | PubMed | Europe PMC | arXiv
 
2025 | Published | Conference Paper | IST-REx-ID: 21324 | OA
S. Bombari and M. Mondelli, “Spurious correlations in high dimensional regression: The roles of regularization, simplicity bias and over-parameterization,” in Proceedings of the 42nd International Conference on Machine Learning, Vancouver, Canada, 2025, vol. 267, pp. 4839–4873.
[Published Version] View | Files available | arXiv
 

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