Marco Mondelli

You can also browse my Google Scholar profile or my CV.

Preprints

• F. Kovačević, E. Cyffers, S. S. Mannelli, and M. Mondelli, "High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning", arXiv preprint, 2026. [LINK]

• G. Kur, I. Rugina, C. Dominé, and M. Mondelli, "Grokking through the Lens of Minimum-Norm Interpolation", arXiv preprint, 2026. [LINK]

• D. Wu, L. Chen, T. Misiakiewicz*, and M. Mondelli*, "When do data mixtures improve scaling laws? Insights from high-dimensional regression", arXiv preprint, 2026. [LINK]

• M. Cairney-Leeming, S. Bombari, and M. Mondelli, "A Sharp Transition in Data Reconstruction under Differential Privacy", arXiv preprint, 2026. [LINK]

• B. Demirel, C. Dominé, V. Maiorca, M. Fumero, M. Mondelli, and F. Locatello, "λ-JEPA Spectral Anti-Collapse Regularization for Self-Supervised Learning", arXiv preprint, 2026. [LINK]

• J. Bajzik*, A. Depope*, Y. Zolfimoselo, A. Sharipov, A. Lesayova, H. Klein, A. Richmond, S. Vernadis, A. Grauslys, S. Andrejev, A. Zelezniak, M. Ralser, R. Marioni, M. Mondelli* and M. Robinson*, "Joint Variable Selection for Omic Biomarkers in Time-to-Event Data", bioRxiv preprint, 2026. [LINK]

• S. Bombari, J. Luo, I. Seroussi and M. Mondelli, "High-Dimensional Private Linear Regression with Optimal Rates", arXiv preprint, 2026. [LINK]

• Y. Zhang, H. C. Ji, R. Venkataramanan and M. Mondelli, "Optimal Estimation in Orthogonally Invariant Generalized Linear Models: Spectral Initialization and Approximate Message Passing", arXiv preprint, 2026. [LINK]

Conference Papers

• V. Njaradi, C. Domineé, R. Swanson, M. Mondelli* and A. Saxe*, "Optimal Representation Size: High-Dimensional Analysis of Pretraining and Linear Probing", in Proceedings of 2026 Conference on Neural Information Processing Systems (NeurIPS'26). [LINK]

• P. Súkeník, C. L. Amado, C. Lampert* and M. Mondelli*, "Sink vs. diagonal patterns as mechanisms for attention switch and oversmoothing prevention", in Proceedings of 2026 Conference on Neural Information Processing Systems (NeurIPS'26). [LINK]

• D. Wu, L. Chen, T. Misiakiewicz* and M. Mondelli*, "Improved Scaling Laws via Weak-to-Strong Generalization in Random Feature Ridge Regression", in Proceedings of 2026 International Conference on Machine Learning (ICML'26). [LINK]

• F. Kovačević, H. C. Ji, D. Wu*, M. Soltanolkotabi* and M. Mondelli*, "Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning", in Proceedings of 2026 International Conference on Machine Learning (ICML'26). [LINK]

• E. Cyffers, A. Mirrokni and M. Mondelli, "Optimal Regularization for Performative Learning", in Proceedings of 2026 International Conference on Machine Learning (ICML'26). [LINK]

• N. Anguita, F. Locatello, A. M. Saxe, M. Mondelli, F. Mancini, S. Lippl and C. Domine, "A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning", in Proceedings of 2026 International Conference on Machine Learning (ICML'26). [LINK]

• P. Rezaei, F. Kovačević, F. Locatello* and M. Mondelli*, "High-dimensional Analysis of Synthetic Data Selection", in Proceedings of International Conference on Learning Representations (ICLR'26). (oral). [LINK]

• L. Iurada*, S. Bombari*, T. Tommasi and M. Mondelli, "A Law of Data Reconstruction for Random Features (and Beyond)", in Proceedings of International Conference on Learning Representations (ICLR'26). [LINK]

• D. Wu*, A. Shevchenko*, S. Oymak and M. Mondelli, "Attention with Trained Embeddings Provably Selects Important Tokens", in Proceedings of 2025 Conference on Neural Information Processing Systems (NeurIPS'25). [LINK]

• P. Súkeník, C. Lampert*, and M. Mondelli*, "Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers", in Proceedings of 2025 Conference on Neural Information Processing Systems (NeurIPS'25). [LINK]

• F. Kovačević, Y. Zhang, and M. Mondelli, "Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery", in Proceedings of 2025 Annual Conference on Learning Theory (COLT'25). [LINK]

• D. Wu and M. Mondelli, "Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime", in Proceedings of 2025 International Conference on Machine Learning (ICML'25). (spotlight). [LINK]

• S. Bombari and M. Mondelli, "Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization", in Proceedings of 2025 International Conference on Machine Learning (ICML'25). Best Paper Award at the Workshop on Spurious Correlation and Shortcut Learning, 2025 International Conference on Learning Representations (SCSL @ICLR'25). [LINK]

• 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 2025 International Conference on Machine Learning (ICML'25). [LINK]

• M. E. 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 Proceedings of International Conference on Learning Representations (ICLR'25). (spotlight). [LINK]

• A. Jacot, P. Súkeník, Z. Wang, and M. Mondelli, "Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse", in Proceedings of International Conference on Learning Representations (ICLR'25). (oral). [LINK]

• Y. Zhang, and M. Mondelli, "Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods", in Proceedings of 2024 Conference on Neural Information Processing Systems (NeurIPS'24). [LINK]

• P. Súkeník, C. Lampert*, and M. Mondelli*, "Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?", in Proceedings of 2024 Conference on Neural Information Processing Systems (NeurIPS'24). [LINK]

• D. Beaglehole, P. Súkeník, M. Mondelli, and M. Belkin, "Average gradient outer product as a mechanism for deep neural collapse", in Proceedings of 2024 Conference on Neural Information Processing Systems (NeurIPS'24). [LINK]

• Y. Zhang, H. C. Ji, R. Venkataramanan, and M. Mondelli, "Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing", extended abstract at 2024 Annual Conference on Learning Theory (COLT'24). [LINK]

• A. R. Esposito and M. Mondelli, "Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo", extended abstract at 2024 Annual Conference on Learning Theory (COLT'24). [LINK]

• S. Bombari and M. Mondelli, "Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features", in Proceedings of 2024 International Conference on Machine Learning (ICML'24). [LINK]

• K. Kögler*, A. Shevchenko*, S. H. Hassani, and M. Mondelli, "Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth", in Proceedings of 2024 International Conference on Machine Learning (ICML'24). [LINK]

• S. Bombari and M. Mondelli, "How Spurious Features are Memorized: Precise Analysis for Random and NTK Features", in Proceedings of 2024 International Conference on Machine Learning (ICML'24). [LINK]

• A. Depope, M. Mondelli*, and M. Robinson*, "Inference of Genetic Effects via Approximate Message Passing", in Proceedings of 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'24).

• P. Súkeník, M. Mondelli*, and C. Lampert*, "Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model", in Proceedings of 2023 Conference on Neural Information Processing Systems (NeurIPS'23). (spotlight). [LINK]

• S. Bombari, S. Kiyani, and M. Mondelli, "Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels", in Proceedings of 2023 International Conference on Machine Learning (ICML'23) (oral). [LINK]

• A. Shevchenko*, K. Kögler*, S. H. Hassani, and M. Mondelli, "Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods", in Proceedings of 2023 International Conference on Machine Learning (ICML'23) (oral). [LINK]

• A. R. Esposito and M. Mondelli, "Concentration without Independence via Information Measures", in Proceedings of 2023 IEEE International Symposium on Information Theory (ISIT'23). [LINK]

• T. Fu, Y. Liu, J. Barbier, M. Mondelli, S. Liang, and T. Hou, "Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise", in Proceedings of 2023 IEEE International Symposium on Information Theory (ISIT'23). [LINK]

• Y. Xu, T. Hou, S. Liang, and M. Mondelli, "Approximate Message Passing for Multi-Layer Estimation in Rotationally Invariant Models", in Proceedings of 2023 IEEE Information Theory Workshop (ITW'23). [LINK]

• S. Bombari, M. H. Amani, and M. Mondelli, "Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization", in Proceedings of 2022 Conference on Neural Information Processing Systems (NeurIPS'22). [LINK]

• J. Barbier*, T. Hou, M. Mondelli*, and M. Sáenz*, "The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation?", in Proceedings of 2022 Conference on Neural Information Processing Systems (NeurIPS'22). [LINK]

• M. H. Amani, S. Bombari, M. Mondelli, R. Pukdee, and S. Rini, "Sharp asymptotics on the compression of two-layer neural networks", in Proceedings of 2022 IEEE Information Theory Workshop (ITW'22). [LINK]

• R. Venkataramanan, K. Kögler, and M. Mondelli, "Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing", in Proceedings of 2022 International Conference on Machine Learning (ICML'22). [LINK]

• D. Fathollahi and M. Mondelli, "Polar Coded Computing: The Role of the Scaling Exponent", in Proceedings of 2022 IEEE International Symposium on Information Theory (ISIT'22). [LINK]

• M. Mondelli, and R. Venkataramanan, "PCA Initialization for Approximate Message Passing in Rotationally Invariant Models", in Proceedings of 2021 Conference on Neural Information Processing Systems (NeurIPS'21). [LINK]

• Q. Nguyen, P. Brechet, and M. Mondelli, "When Are Solutions Connected in Deep Networks?", in Proceedings of 2021 Conference on Neural Information Processing Systems (NeurIPS'21). [LINK]

• S. A. Hashemi, M. Mondelli, J. Cioffi, and A. Goldsmith, "Successive Syndrome-Check Decoding of Polar Codes", in Proceedings of 2021 Asilomar Conference on Signals, Systems, and Computers. [LINK]

• Q. Nguyen, M. Mondelli and G. Montufar, "Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks", in Proceedings of 2021 International Conference on Machine Learning (ICML'21). [LINK]

• D. Fathollahi, N. Farsad, S. A. Hashemi, and M. Mondelli, "Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes", in Proceedings of 2021 IEEE International Symposium on Information Theory (ISIT'21). [LINK]

• S. A. Hashemi, M. Mondelli, A. Fazeli, A. Vardy, J. Cioffi, and A. Goldsmith, "Parallelism versus Latency in Simplified Successive-Cancellation Decoding of Polar Codes", in Proceedings of 2021 IEEE International Symposium on Information Theory (ISIT'21). [LINK]

• M. Mondelli, and R. Venkataramanan, "Approximate Message Passing with Spectral Initialization for Generalized Linear Models", in Proceedings of 2021 International Conference on Artificial Intelligence and Statistics (AISTATS'21). [LINK]

• Q. Nguyen and M. Mondelli, "Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology", in Proceedings of 2020 Conference on Neural Information Processing Systems (NeurIPS'20). [LINK]

• A. Shevchenko and M. Mondelli, "Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks", in Proceedings of 2020 International Conference on Machine Learning (ICML'20). [LINK]

• M. Mondelli, S. A. Hashemi, J. Cioffi, and A. Goldsmith, "Sublinear Latency for Simplified Successive Cancellation Decoding of Polar Codes", in Proceedings of 2020 IEEE International Symposium on Information Theory (ISIT'20). [LINK]

• M. Mondelli, and A. Montanari, "On the Connection Between Learning Two-Layers Neural Networks and Tensor Decomposition", in Proceedings of 2019 International Conference on Artificial Intelligence and Statistics (AISTATS'19). [LINK]

• S. A. Hashemi, C. Condo, M. Mondelli, and W. J. Gross, "Rate-Flexible Fast Polar Decoders", in Proceedings of 2019 IEEE Information Theory Workshop (ITW'19). [LINK]

• M. Mondelli, and A. Montanari, "Fundamental Limits of Weak Recovery with Applications to Phase Retrieval", extended abstract at 2018 Conference on Learning Theory (COLT'18). [LINK]

• S. A. Hashemi, N. Doan, M. Mondelli, and W. J. Gross, "Decoding Reed-Muller and Polar Codes by Successive Factor Graph Permutations", in Proceedings of 2018 International Symposium on Turbo Codes & Iterative Information Processing (ISTC'18). [LINK]

• N. Doan, S. A. Hashemi, M. Mondelli, and W. J. Gross, "On the Decoding of Polar Codes on Permuted Factor Graphs", in Proceedings of 2018 IEEE Global Communications Conference (GLOBECOM'18). [LINK]

• A. Fazeli, S. H. Hassani, M. Mondelli, and A. Vardy, "Binary Linear Codes with Optimal Scaling: Polar Codes with Large Kernels", in Proceedings of 2018 IEEE Information Theory Workshop (ITW'18). [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "A New Coding Paradigm for the Primitive Relay Channel", in Proceedings of 2018 IEEE International Symposium on Information Theory (ISIT'18). [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "Construction of Polar Codes with Sublinear Complexity", in Proceedings of 2017 IEEE International Symposium on Information Theory (ISIT'17). [LINK]

• S. A. Hashemi, M. Mondelli, S. H. Hassani, R. Urbanke, and W. J. Gross, "Partitioned List Decoding of Polar Codes: Analysis and Improvement of Finite Length Performance", in Proceedings of 2017 IEEE Global Communications Conference (GLOBECOM'17). [LINK]

• M. Mondelli, S. H. Hassani, I. Marić, D. Hui, and S.-N. Hong, "Capacity-Achieving Rate-Compatible Polar Codes for General Channels", in Proceedings of 2017 IEEE Wireless Communications and Networking Conference Workshops (WCNCW'17). [LINK]

• S. Kudekar, S. Kumar, M. Mondelli, H. D. Pfister, E. Şaşoğlu, and R. Urbanke, "Reed-Muller Codes Achieve Capacity on Erasure Channels", Best Paper Award at 2016 ACM Symposium on Theory of Computing (STOC'16). [LINK]

• S. Kudekar, S. Kumar, M. Mondelli, H. D. Pfister, and R. Urbanke, "Comparing the Bit-MAP and Block-MAP Decoding Thresholds of Reed-Muller Codes on BMS Channels", in Proceedings of 2016 IEEE International Symposium on Information Theory (ISIT'16). [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "Unified Scaling of Polar Codes: Error Exponent, Scaling Exponent, Moderate Deviations, and Error Floors", Student Paper Award at 2015 IEEE International Symposium on Information Theory (ISIT'15). [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "How to Achieve the Capacity of Asymmetric Channels", in Proceedings of 2014 Allerton Conference on Communication, Control, and Computing (ALLERTON'14). [LINK]

• M. Mondelli, S. H. Hassani, I. Sason, and R. Urbanke, "Achieving Marton's Region for Broadcast Channels Using Polar Codes", in Proceedings of 2014 IEEE International Symposium on Information Theory (ISIT'14). [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "From Polar to Reed-Muller Codes: a Technique to Improve the Finite-Length Performance", in Proceedings of 2014 IEEE International Symposium on Information Theory (ISIT'14). [LINK]

• M. Mondelli, S. H. Hassani, I. Sason, and R. Urbanke, "Scaling Exponent of List Decoders with Applications to Polar Codes", in Proceedings of 2013 IEEE Information Theory Workshop (ITW'13). [LINK]

• M. Mondelli, Q. Zhou, X. Ma, and V. Lottici, "A Cooperative Approach for Amplify-and-Forward Differential Transmitted Reference IR-UWB Relay Systems", in Proceedings of 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'12). [LINK]

Journal Papers

• A. Depope, J. Bajzik, M. Mondelli*, and M. Robinson*, "Joint modelling of whole genome sequence data for human height via approximate message passing", in Cell Genomics, 2026. [LINK]

• Y. Zhang, M. Mondelli, and R. Venkataramanan, "Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models", in SIAM Journal on Mathematics of Data Science (SIMODS), 2026. [LINK]

• Y. Zhang, H. C. Ji, R. Venkataramanan, and M. Mondelli, "Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing", in Mathematical Statistics and Learning (MSL), 2025. [LINK]

• S. Bombari and M. Mondelli, "Privacy for Free in the Over-Parameterized Regime", in Proceedings of the National Academy of Sciences (PNAS), 2025. [LINK]

• M. Fornasier, T. Klock, M. Mondelli, and M. Rauchensteiner, "Efficient Identification of Wide Shallow Neural Networks with Biases", in Applied and Computational Harmonic Analysis (ACHA), 2025. [LINK]

• J. Barbier, F. Camilli, M. Mondelli, and Y. Xu, "Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise", in Physical Review Research (PRR), 2025. [LINK]

• F. Pedrotti, J. Maas, and M. Mondelli, "Improved Convergence of Score-Based Diffusion Models via Prediction-Correction", in Transactions on Machine Learning Research (TMLR), 2024. Awarded a J2C Certification. Also presented at the NeurIPS 2023 Workshop on Diffusion Models. [LINK]

• A. R. Esposito and M. Mondelli, "Concentration without Independence via Information Measures", in IEEE Transactions on Information Theory, 2024. [LINK]

• J. Barbier, F. Camilli, M. Mondelli, and M. Saenz, "Fundamental limits in structured principal component analysis and how to reach them", in Proceedings of the National Academy of Sciences (PNAS), 2023. [LINK]

• D. Wu, V. Kungurtsev, and M. Mondelli, "Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence", in Transactions on Machine Learning Research (TMLR), 2023. Also presented at the OPT 2022 NeurIPS workshop. [LINK]

• N. Doan, S. A. Hashemi, M. Mondelli, and W. J. Gross, "Decoding Reed-Muller Codes with Successive Codeword Permutations", in IEEE Transactions on Wireless Communications, 2022. [LINK]

• M. Mondelli, and R. Venkataramanan, "Approximate Message Passing with Spectral Initialization for Generalized Linear Models", invited paper for the 2022 Machine Learning Special Issue of the Journal of Statistical Mechanics, Theory and Experiment (JSTAT). [LINK]

• A. Shevchenko, V. Kungurtsev, and M. Mondelli, "Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks", in Journal of Machine Learning Research (JMLR), 2022. [LINK]

• S. A. Hashemi, M. Mondelli, A. Fazeli, A. Vardy, J. Cioffi, and A. Goldsmith, "Parallelism versus Latency in Simplified Successive-Cancellation Decoding of Polar Codes", in IEEE Transactions on Wireless Communications, 2022. [LINK]

• M. Mondelli, C. Thrampoulidis, and R. Venkataramanan, "Optimal Combination of Linear and Spectral Estimators for Generalized Linear Models", in Foundations of Computational Mathematics, 2021. [LINK]

• A. Fazeli, S. H. Hassani, M. Mondelli, and A. Vardy, "Binary Linear Codes with Optimal Scaling: Polar Codes with Large Kernels", in IEEE Transactions on Information Theory, 2021. [LINK]

• M. Mondelli, S. A. Hashemi, J. Cioffi, and A. Goldsmith, "Sublinear Latency for Simplified Successive Cancellation Decoding of Polar Codes", in IEEE Transactions on Wireless Communications, 2021. [LINK]

• A. Javanmard, M. Mondelli, and A. Montanari, "Analysis of a Two-Layer Neural Network via Displacement Convexity", in Annals of Statistics, 2020. [LINK]

• S. A. Hashemi, C. Condo, M. Mondelli, and W. J. Gross, "Rate-Flexible Fast Polar Decoders", in IEEE Transactions on Signal Processing, 2019. [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "A New Coding Paradigm for the Primitive Relay Channel", in Algorithms, 2019. [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "Construction of Polar Codes with Sublinear Complexity", in IEEE Transactions on Information Theory, 2019. [LINK]

• M. Mondelli, and A. Montanari, "Fundamental Limits of Weak Recovery with Applications to Phase Retrieval", in Foundations of Computational Mathematics, 2018. [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "How to Achieve the Capacity of Asymmetric Channels", in IEEE Transactions on Information Theory, 2018. [LINK]

• S. A. Hashemi, M. Mondelli, S. H. Hassani, C. Condo, R. Urbanke, and W. J. Gross, "Decoder Partitioning: Towards Practical List Decoding of Polar Codes", in IEEE Transactions on Communications, 2018. [LINK]

• S. Kudekar, S. Kumar, M. Mondelli, H. D. Pfister, E. Şaşoğlu, and R. Urbanke, "Reed-Muller Codes Achieve Capacity on Erasure Channels", in IEEE Transactions on Information Theory, 2017. [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "Unified Scaling of Polar Codes: Error Exponent, Scaling Exponent, Moderate Deviations, and Error Floors", in IEEE Transactions on Information Theory, 2016. [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "Scaling Exponent of List Decoders with Applications to Polar Codes", in IEEE Transactions on Information Theory, 2015. [LINK]

• M. Mondelli, S. H. Hassani, I. Sason, and R. Urbanke, "Achieving Marton's Region for Broadcast Channels Using Polar Codes", in IEEE Transactions on Information Theory, 2015. [LINK]

• M. Mondelli, S. H. Hassani, and R. Urbanke, "From Polar to Reed-Muller Codes: a Technique to Improve the Finite-Length Performance", in IEEE Transactions on Communications, 2014. [LINK]

• M. Mondelli, Q. Zhou, V. Lottici, and X. Ma, "Joint Power Allocation and Path Selection for Multi-Hop Noncoherent Decode and Forward UWB Communications", in IEEE Transactions on Wireless Communications, 2014. [LINK]

Thesis

• M. Mondelli, "From Polar to Reed-Muller Codes: Unified Scaling, Non-standard Channels, and a Proven Conjecture", Ph.D. Thesis, EPFL, recipient of the 2018 EPFL Doctorate Award and of the 2017 Patrick Denantes Memorial Prize. [LINK]