| Reed-Muller codes achieve capacity on erasure channels S Kudekar, S Kumar, M Mondelli, HD Pfister, E Şaşoğlu, R Urbanke Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, 2016 | 195* | 2016 |
| Unified scaling of polar codes: Error exponent, scaling exponent, moderate deviations, and error floors M Mondelli, SH Hassani, RL Urbanke IEEE Transactions on Information Theory 62 (12), 6698-6712, 2016 | 140 | 2016 |
| From polar to Reed-Muller codes: A technique to improve the finite-length performance M Mondelli, SH Hassani, RL Urbanke IEEE Transactions on Communications 62 (9), 3084-3091, 2014 | 136 | 2014 |
| Fundamental limits of weak recovery with applications to phase retrieval M Mondelli, A Montanari Conference On Learning Theory, 1445-1450, 2018 | 111 | 2018 |
| How to achieve the capacity of asymmetric channels M Mondelli, SH Hassani, RL Urbanke IEEE Transactions on Information Theory 64 (5), 3371-3393, 2018 | 96* | 2018 |
| Achieving Marton’s region for broadcast channels using polar codes M Mondelli, SH Hassani, I Sason, RL Urbanke IEEE Transactions on Information Theory 61 (2), 783-800, 2014 | 94 | 2014 |
| On the decoding of polar codes on permuted factor graphs N Doan, SA Hashemi, M Mondelli, WJ Gross 2018 IEEE Global Communications Conference (GLOBECOM), 1-6, 2018 | 80 | 2018 |
| Construction of polar codes with sublinear complexity M Mondelli, SH Hassani, RL Urbanke IEEE Transactions on Information Theory 65 (5), 2782-2791, 2018 | 69 | 2018 |
| Analysis of a two-layer neural network via displacement convexity A Javanmard, M Mondelli, A Montanari | 63 | 2020 |
| Binary linear codes with optimal scaling: Polar codes with large kernels A Fazeli, H Hassani, M Mondelli, A Vardy IEEE Transactions on Information Theory 67 (9), 5693-5710, 2020 | 59* | 2020 |
| Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology Q Nguyen, M Mondelli Advances in Neural Information Processing Systems (NeurIPS), 2020, 2020 | 53 | 2020 |
| On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition M Mondelli, A Montanari International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019 | 53 | 2019 |
| Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks Q Nguyen, M Mondelli, G Montufar International Conference on Machine Learning (ICML), 2021, 2021 | 51 | 2021 |
| Decoder partitioning: Towards practical list decoding of polar codes SA Hashemi, M Mondelli, SH Hassani, C Condo, RL Urbanke, WJ Gross IEEE Transactions on Communications 66 (9), 3749-3759, 2018 | 50 | 2018 |
| Scaling exponent of list decoders with applications to polar codes M Mondelli, SH Hassani, RL Urbanke IEEE Transactions on Information Theory 61 (9), 4838-4851, 2015 | 49 | 2015 |
| Decoding Reed-Muller and polar codes by successive factor graph permutations SA Hashemi, N Doan, M Mondelli, WJ Gross 2018 IEEE 10th International Symposium on Turbo Codes & Iterative …, 2018 | 48 | 2018 |
| Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks A Shevchenko, M Mondelli International Conference on Machine Learning (ICML), 2020, 2020 | 33 | 2020 |
| Comparing the bit-MAP and block-MAP decoding thresholds of Reed-Muller codes on BMS channels S Kudekar, S Kumar, M Mondelli, HD Pfister, R Urbankez 2016 IEEE International Symposium on Information Theory (ISIT), 1755-1759, 2016 | 30 | 2016 |
| Partitioned list decoding of polar codes: Analysis and improvement of finite length performance SA Hashemi, M Mondelli, SH Hassani, R Urbanke, WJ Gross GLOBECOM 2017-2017 IEEE Global Communications Conference, 1-7, 2017 | 29 | 2017 |
| Approximate message passing with spectral initialization for generalized linear models M Mondelli, R Venkataramanan Journal of Statistical Mechanics: Theory and Experiment 2022 (11), 114003, 2022 | 26 | 2022 |
Hamed HassaniElectrical Engineering, Computer Science, and Statistics; University of PennsylvaniaVerified email at seas.upenn.edu
