Marco Mondelli

Mondelli Group

60 Publications

Mark all

[60]
2024 | Journal Article | IST-REx-ID: 15172
Esposito, A. R., & Mondelli, M. (n.d.). Concentration without independence via information measures. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/TIT.2024.3367767
View | Files available | DOI | arXiv
 
[59]
2023 | Journal Article | IST-REx-ID: 13315 | OA
Barbier, J., Camilli, F., Mondelli, M., & Sáenz, M. (2023). Fundamental limits in structured principal component analysis and how to reach them. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2302028120
[Published Version] View | Files available | DOI | PubMed | Europe PMC
 
[58]
2023 | Conference Paper | IST-REx-ID: 14459 | OA
Shevchenko, A., Kögler, K., Hassani, H., & Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[57]
2023 | Conference Paper | IST-REx-ID: 13321 | OA
Xu, Y., Hou, T. Q., Liang, S. S., & Mondelli, M. (2023). Approximate message passing for multi-layer estimation in rotationally invariant models. In 2023 IEEE Information Theory Workshop (pp. 294–298). Saint-Malo, France: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITW55543.2023.10160238
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[56]
2023 | Conference Paper | IST-REx-ID: 12859 | OA
Bombari, S., Kiyani, S., & Mondelli, M. (2023). Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 2738–2776). Honolulu, HI, United States: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[55]
2023 | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník, P., Mondelli, M., & Lampert, C. (n.d.). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[54]
2023 | Conference Paper | IST-REx-ID: 14924 | OA
Wu, D., Kungurtsev, V., & Mondelli, M. (2023). Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. In Transactions on Machine Learning Research. ML Research Press.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[53]
2023 | Conference Paper | IST-REx-ID: 14923 | OA
Fu, T., Liu, Y., Barbier, J., Mondelli, M., Liang, S., & Hou, T. (n.d.). Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. In Proceedings of 2023 IEEE International Symposium on Information Theory. Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206671
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[52]
2023 | Conference Paper | IST-REx-ID: 14922 | OA
Esposito, A. R., & Mondelli, M. (2023). Concentration without independence via information measures. In Proceedings of 2023 IEEE International Symposium on Information Theory (pp. 400–405). Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206899
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[51]
2022 | Journal Article | IST-REx-ID: 11420 | OA
Shevchenko, A., Kungurtsev, V., & Mondelli, M. (2022). Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
[Published Version] View | Files available | arXiv
 
[50]
2022 | Conference Paper | IST-REx-ID: 12016 | OA
Fathollahi, D., & Mondelli, M. (2022). Polar coded computing: The role of the scaling exponent. In 2022 IEEE International Symposium on Information Theory (Vol. 2022, pp. 2154–2159). Espoo, Finland: IEEE. https://doi.org/10.1109/ISIT50566.2022.9834712
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[49]
2022 | Conference Paper | IST-REx-ID: 12540 | OA
Venkataramanan, R., Kögler, K., & Mondelli, M. (2022). Estimation in rotationally invariant generalized linear models via approximate message passing. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162). Baltimore, MD, United States: ML Research Press.
[Published Version] View | Files available
 
[48]
2022 | Preprint | IST-REx-ID: 12536 | OA
Barbier, J., Hou, T., Mondelli, M., & Saenz, M. (n.d.). The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? arXiv. https://doi.org/10.48550/arXiv.2205.10009
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[47]
2022 | Journal Article | IST-REx-ID: 12233 | OA
Doan, N., Hashemi, S. A., Mondelli, M., & Gross, W. J. (2022). Decoding Reed-Muller codes with successive codeword permutations. IEEE Transactions on Communications. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tcomm.2022.3211101
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[46]
2022 | Journal Article | IST-REx-ID: 10364 | OA
Hashemi, S. A., Mondelli, M., Fazeli, A., Vardy, A., Cioffi, J., & Goldsmith, A. (2022). Parallelism versus latency in simplified successive-cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/TWC.2021.3125626
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[45]
2022 | Journal Article | IST-REx-ID: 12538 | OA
Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., & Rini, S. (2022). Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop. Mumbai, India: IEEE. https://doi.org/10.1109/ITW54588.2022.9965870
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[44]
2022 | Conference Paper | IST-REx-ID: 12537 | OA
Bombari, S., Amani, M. H., & Mondelli, M. (2022). Memorization and optimization in deep neural networks with minimum over-parameterization. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 7628–7640). Curran Associates.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[43]
2022 | Journal Article | IST-REx-ID: 12480 | OA
Mondelli, M., & Venkataramanan, R. (2022). Approximate message passing with spectral initialization for generalized linear models. Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing. https://doi.org/10.1088/1742-5468/ac9828
[Published Version] View | Files available | DOI | WoS
 
[42]
2021 | Conference Paper | IST-REx-ID: 10595 | OA
Nguyen, Q., Mondelli, M., & Montufar, G. F. (2021). Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. In M. Meila & T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[41]
2021 | Conference Paper | IST-REx-ID: 10599 | OA
Hashemi, S. A., Mondelli, M., Cioffi, J., & Goldsmith, A. (2021). Successive syndrome-check decoding of polar codes. In Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers (Vol. 2021–October, pp. 943–947). Virtual, Pacific Grove, CA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IEEECONF53345.2021.9723394
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[40]
2021 | Conference Paper | IST-REx-ID: 13146 | OA
Nguyen, Q., Mondelli, M., & Montufar, G. (2021). Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.
[Published Version] View | Files available | arXiv
 
[39]
2021 | Journal Article | IST-REx-ID: 9047 | OA
Mondelli, M., Hashemi, S. A., Cioffi, J. M., & Goldsmith, A. (2021). Sublinear latency for simplified successive cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. IEEE. https://doi.org/10.1109/TWC.2020.3022922
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[38]
2021 | Conference Paper | IST-REx-ID: 10053 | OA
Hashemi, S. A., Mondelli, M., Fazeli, A., Vardy, A., Cioffi, J., & Goldsmith, A. (2021). Parallelism versus latency in simplified successive-cancellation decoding of polar codes. In 2021 IEEE International Symposium on Information Theory (pp. 2369–2374). Melbourne, Australia: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISIT45174.2021.9518153
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[37]
2021 | Conference Paper | IST-REx-ID: 10597 | OA
Fathollahi, D., Farsad, N., Hashemi, S. A., & Mondelli, M. (2021). Sparse multi-decoder recursive projection aggregation for Reed-Muller codes. In 2021 IEEE International Symposium on Information Theory (pp. 1082–1087). Virtual, Melbourne, Australia: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/isit45174.2021.9517887
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[36]
2021 | Journal Article | IST-REx-ID: 10211 | OA
Mondelli, M., Thrampoulidis, C., & Venkataramanan, R. (2021). Optimal combination of linear and spectral estimators for generalized linear models. Foundations of Computational Mathematics. Springer. https://doi.org/10.1007/s10208-021-09531-x
[Published Version] View | Files available | DOI | WoS | arXiv
 
[35]
2021 | Conference Paper | IST-REx-ID: 10593 | OA
Mondelli, M., & Venkataramanan, R. (2021). PCA initialization for approximate message passing in rotationally invariant models. In 35th Conference on Neural Information Processing Systems (Vol. 35, pp. 29616–29629). Virtual: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[34]
2021 | Conference Paper | IST-REx-ID: 10594 | OA
Nguyen, Q., Bréchet, P., & Mondelli, M. (2021). When are solutions connected in deep networks? In 35th Conference on Neural Information Processing Systems (Vol. 35). Virtual: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[33]
2021 | Conference Paper | IST-REx-ID: 10598 | OA
Mondelli, M., & Venkataramanan, R. (2021). Approximate message passing with spectral initialization for generalized linear models. In A. Banerjee & K. Fukumizu (Eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Vol. 130, pp. 397–405). Virtual, San Diego, CA, United States: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[32]
2021 | Journal Article | IST-REx-ID: 9002
Fazeli, A., Hassani, H., Mondelli, M., & Vardy, A. (2021). Binary linear codes with optimal scaling: Polar codes with large kernels. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/TIT.2020.3038806
[Preprint] View | Files available | DOI | arXiv
 
[31]
2020 | Conference Paper | IST-REx-ID: 9221 | OA
Nguyen, Q., & Mondelli, M. (2020). Global convergence of deep networks with one wide layer followed by pyramidal topology. In 34th Conference on Neural Information Processing Systems (Vol. 33, pp. 11961–11972). Vancouver, Canada: Curran Associates.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[30]
2020 | Conference Paper | IST-REx-ID: 8536 | OA
Mondelli, M., Hashemi, S. A., Cioffi, J., & Goldsmith, A. (2020). Simplified successive cancellation decoding of polar codes has sublinear latency. In IEEE International Symposium on Information Theory - Proceedings (Vol. 2020–June). Los Angeles, CA, United States: IEEE. https://doi.org/10.1109/ISIT44484.2020.9174141
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[29]
2020 | Conference Paper | IST-REx-ID: 9198 | OA
Shevchenko, A., & Mondelli, M. (2020). Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 8773–8784). ML Research Press.
[Published Version] View | Files available | arXiv
 
[28]
2020 | Journal Article | IST-REx-ID: 6748 | OA
Javanmard, A., Mondelli, M., & Montanari, A. (2020). Analysis of a two-layer neural network via displacement convexity. Annals of Statistics. Institute of Mathematical Statistics. https://doi.org/10.1214/20-AOS1945
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[27]
2019 | Journal Article | IST-REx-ID: 6662 | OA
Mondelli, M., & Montanari, A. (2019). Fundamental limits of weak recovery with applications to phase retrieval. Foundations of Computational Mathematics. Springer. https://doi.org/10.1007/s10208-018-9395-y
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[26]
2019 | Journal Article | IST-REx-ID: 6663 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2019). Construction of polar codes with sublinear complexity. IEEE. IEEE. https://doi.org/10.1109/tit.2018.2889667
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[25]
2019 | Conference Paper | IST-REx-ID: 6747 | OA
Mondelli, M., & Montanari, A. (2019). On the connection between learning two-layers neural networks and tensor  decomposition. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (Vol. 89, pp. 1051–1060). Naha, Okinawa, Japan: Proceedings of Machine Learning Research.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[24]
2019 | Journal Article | IST-REx-ID: 6750 | OA
Hashemi, S. A., Condo, C., Mondelli, M., & Gross, W. J. (2019). Rate-flexible fast polar decoders. IEEE Transactions on Signal Processing. IEEE. https://doi.org/10.1109/TSP.2019.2944738
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[23]
2019 | Journal Article | IST-REx-ID: 7007 | OA
Mondelli, M., Hassani, S. H., & Urbanke, R. (2019). A new coding paradigm for the primitive relay channel. Algorithms. MDPI. https://doi.org/10.3390/a12100218
[Published Version] View | Files available | DOI | arXiv
 
[22]
2018 | Conference Paper | IST-REx-ID: 6664 | OA
Hashemi, S. A., Doan, N., Mondelli, M., & Gross, W. (2018). Decoding Reed-Muller and polar codes by successive factor graph permutations. In 2018 IEEE 10th International Symposium on Turbo Codes & Iterative Information Processing (pp. 1–5). Hong Kong, China: IEEE. https://doi.org/10.1109/istc.2018.8625281
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[21]
2018 | Journal Article | IST-REx-ID: 6674
Hashemi, S. A., Mondelli, M., Hassani, S. H., Condo, C., Urbanke, R. L., & Gross, W. J. (2018). Decoder partitioning: Towards practical list decoding of polar codes. IEEE Transactions on Communications. IEEE. https://doi.org/10.1109/tcomm.2018.2832207
View | DOI
 
[20]
2018 | Conference Paper | IST-REx-ID: 6728 | OA
Doan, N., Hashemi, S. A., Mondelli, M., & Gross, W. J. (2018). On the decoding of polar codes on permuted factor graphs. In 2018 IEEE Global Communications Conference . Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/glocom.2018.8647308
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[19]
2018 | Journal Article | IST-REx-ID: 6678 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2018). How to achieve the capacity of asymmetric channels. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2018.2789885
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[18]
2018 | Conference Paper | IST-REx-ID: 6675 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2018). A new coding paradigm for the primitive relay channel. In 2018 IEEE International Symposium on Information Theory (pp. 351–355). Vail, CO, United States: IEEE. https://doi.org/10.1109/isit.2018.8437479
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[17]
2018 | Conference Paper | IST-REx-ID: 6665 | OA
Fazeli, A., Hassani, H., Mondelli, M., & Vardy, A. (2018). Binary linear codes with optimal scaling: Polar codes with large kernels. In 2018 IEEE Information Theory Workshop (pp. 1–5). Guangzhou, China: IEEE. https://doi.org/10.1109/itw.2018.8613428
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[16]
2017 | Conference Paper | IST-REx-ID: 6679 | OA
Hashemi, S. A., Mondelli, M., Hassani, H., Urbanke, R., & Gross, W. (2017). Partitioned list decoding of polar codes: Analysis and improvement of finite length performance. In 2017 IEEE Global Communications Conference (pp. 1–7). Singapore, Singapore: IEEE. https://doi.org/10.1109/glocom.2017.8254940
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[15]
2017 | Conference Paper | IST-REx-ID: 6729 | OA
Mondelli, M., Hassani, S. H., & Urbanke, R. (2017). Construction of polar codes with sublinear complexity. In 2017 IEEE International Symposium on Information Theory (pp. 1853–1857). Aachen, Germany: IEEE. https://doi.org/10.1109/isit.2017.8006850
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[14]
2017 | Journal Article | IST-REx-ID: 6730 | OA
Kudekar, S., Kumar, S., Mondelli, M., Pfister, H. D., Sasoglu, E., & Urbanke, R. L. (2017). Reed–Muller codes achieve capacity on erasure channels. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2017.2673829
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[13]
2017 | Conference Paper | IST-REx-ID: 6731 | OA
Mondelli, M., Hassani, H., Maric, I., Hui, D., & Hong, S.-N. (2017). Capacity-achieving rate-compatible polar codes for general channels. In 2017 IEEE Wireless Communications and Networking Conference Workshops . San Francisco, CA, USA: IEEE. https://doi.org/10.1109/wcncw.2017.7919107
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[12]
2016 | Journal Article | IST-REx-ID: 6732 | OA
Mondelli, M., Hassani, S. H., & Urbanke, R. L. (2016). Unified scaling of polar codes: Error exponent, scaling exponent, moderate deviations, and error floors. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2016.2616117
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[11]
2016 | Conference Paper | IST-REx-ID: 6733 | OA
Kudekar, S., Kumar, S., Mondelli, M., Pfister, H. D., & Urbankez, R. (2016). Comparing the bit-MAP and block-MAP decoding thresholds of Reed-Muller codes on BMS channels. In 2016 IEEE International Symposium on Information Theory (pp. 1755–1759). Barcelona, Spain: IEEE. https://doi.org/10.1109/isit.2016.7541600
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[10]
2016 | Conference Paper | IST-REx-ID: 6770
Mondelli, M., Kudekar, S., Kumar, S., Pfister, H. D., Şaşoğlu, E., & Urbanke, R. (2016). Reed-Muller codes: Thresholds and weight distribution. In 24th International Zurich Seminar on Communications (p. 50). Zurich, Switzerland: ETH Zürich. https://doi.org/10.3929/ETHZ-A-010646484
View | DOI
 
[9]
2015 | Journal Article | IST-REx-ID: 6737 | OA
Mondelli, M., Hassani, H., Sason, I., & Urbanke, R. (2015). Achieving Marton’s region for broadcast channels using polar codes. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2014.2368555
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[8]
2015 | Journal Article | IST-REx-ID: 6736 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2015). Scaling exponent of list decoders with applications to polar codes. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2015.2453315
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[7]
2014 | Conference Paper | IST-REx-ID: 6740 | OA
Mondelli, M., Urbanke, R., & Hassani, H. (2014). How to achieve the capacity of asymmetric channels. In 52nd Annual Allerton Conference on Communication, Control, and Computing (pp. 789–796). Monticello, IL, United States: IEEE. https://doi.org/10.1109/allerton.2014.7028535
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[6]
2014 | Journal Article | IST-REx-ID: 6739 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2014). From polar to Reed-Muller codes: A technique to improve the finite-length performance. IEEE Transactions on Communications. IEEE. https://doi.org/10.1109/tcomm.2014.2345069
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[5]
2014 | Journal Article | IST-REx-ID: 6744
Mondelli, M., Zhou, Q., Lottici, V., & Ma, X. (2014). Joint power allocation and path selection for multi-hop noncoherent decode and forward UWB communications. IEEE Transactions on Wireless Communications. IEEE. https://doi.org/10.1109/twc.2014.020914.130669
View | DOI
 
[4]
2013 | Journal Article | IST-REx-ID: 6768 | OA
Mondelli, M. (2013). A finite difference scheme for the stack filter simulating the MCM. Image Processing On Line. Image Processing On Line. https://doi.org/10.5201/ipol.2013.53
[Published Version] View | Files available | DOI
 
[3]
2012 | Conference Paper | IST-REx-ID: 6746
Mondelli, M., Zhou, Q., Ma, X., & Lottici, V. (2012). A cooperative approach for amplify-and-forward differential transmitted reference IR-UWB relay systems. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2905–2908). Kyoto, Japan: IEEE. https://doi.org/10.1109/icassp.2012.6288524
View | DOI
 
[2]
2011 | Journal Article | IST-REx-ID: 6749 | OA
Mondelli, M., & Ciomaga, A. (2011). Finite difference schemes for MCM and AMSS. Image Processing On Line. IPOL Image Processing On Line. https://doi.org/10.5201/ipol.2011.cm_fds
[Published Version] View | Files available | DOI
 
[1]
2011 | Conference Paper | IST-REx-ID: 6767
Mondelli, M., & Ciomaga, A. (2011). On finite difference schemes for curvature motions. In Proceedings of the International Student Conference on Pure and Applied Mathematics (pp. 137–156). Iasi, Romania: Editura Universitãtii „Alexandru Ioan Cuza” Iasi. https://doi.org/10.13140/2.1.1862.4646
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60 Publications

Mark all

[60]
2024 | Journal Article | IST-REx-ID: 15172
Esposito, A. R., & Mondelli, M. (n.d.). Concentration without independence via information measures. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/TIT.2024.3367767
View | Files available | DOI | arXiv
 
[59]
2023 | Journal Article | IST-REx-ID: 13315 | OA
Barbier, J., Camilli, F., Mondelli, M., & Sáenz, M. (2023). Fundamental limits in structured principal component analysis and how to reach them. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2302028120
[Published Version] View | Files available | DOI | PubMed | Europe PMC
 
[58]
2023 | Conference Paper | IST-REx-ID: 14459 | OA
Shevchenko, A., Kögler, K., Hassani, H., & Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[57]
2023 | Conference Paper | IST-REx-ID: 13321 | OA
Xu, Y., Hou, T. Q., Liang, S. S., & Mondelli, M. (2023). Approximate message passing for multi-layer estimation in rotationally invariant models. In 2023 IEEE Information Theory Workshop (pp. 294–298). Saint-Malo, France: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ITW55543.2023.10160238
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[56]
2023 | Conference Paper | IST-REx-ID: 12859 | OA
Bombari, S., Kiyani, S., & Mondelli, M. (2023). Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 2738–2776). Honolulu, HI, United States: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[55]
2023 | Conference Paper | IST-REx-ID: 14921 | OA
Súkeník, P., Mondelli, M., & Lampert, C. (n.d.). Deep neural collapse is provably optimal for the deep unconstrained features model. In 37th Annual Conference on Neural Information Processing Systems. New Orleans, LA, United States.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[54]
2023 | Conference Paper | IST-REx-ID: 14924 | OA
Wu, D., Kungurtsev, V., & Mondelli, M. (2023). Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. In Transactions on Machine Learning Research. ML Research Press.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[53]
2023 | Conference Paper | IST-REx-ID: 14923 | OA
Fu, T., Liu, Y., Barbier, J., Mondelli, M., Liang, S., & Hou, T. (n.d.). Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. In Proceedings of 2023 IEEE International Symposium on Information Theory. Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206671
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[52]
2023 | Conference Paper | IST-REx-ID: 14922 | OA
Esposito, A. R., & Mondelli, M. (2023). Concentration without independence via information measures. In Proceedings of 2023 IEEE International Symposium on Information Theory (pp. 400–405). Taipei, Taiwan: IEEE. https://doi.org/10.1109/isit54713.2023.10206899
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[51]
2022 | Journal Article | IST-REx-ID: 11420 | OA
Shevchenko, A., Kungurtsev, V., & Mondelli, M. (2022). Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
[Published Version] View | Files available | arXiv
 
[50]
2022 | Conference Paper | IST-REx-ID: 12016 | OA
Fathollahi, D., & Mondelli, M. (2022). Polar coded computing: The role of the scaling exponent. In 2022 IEEE International Symposium on Information Theory (Vol. 2022, pp. 2154–2159). Espoo, Finland: IEEE. https://doi.org/10.1109/ISIT50566.2022.9834712
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[49]
2022 | Conference Paper | IST-REx-ID: 12540 | OA
Venkataramanan, R., Kögler, K., & Mondelli, M. (2022). Estimation in rotationally invariant generalized linear models via approximate message passing. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162). Baltimore, MD, United States: ML Research Press.
[Published Version] View | Files available
 
[48]
2022 | Preprint | IST-REx-ID: 12536 | OA
Barbier, J., Hou, T., Mondelli, M., & Saenz, M. (n.d.). The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? arXiv. https://doi.org/10.48550/arXiv.2205.10009
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[47]
2022 | Journal Article | IST-REx-ID: 12233 | OA
Doan, N., Hashemi, S. A., Mondelli, M., & Gross, W. J. (2022). Decoding Reed-Muller codes with successive codeword permutations. IEEE Transactions on Communications. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tcomm.2022.3211101
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[46]
2022 | Journal Article | IST-REx-ID: 10364 | OA
Hashemi, S. A., Mondelli, M., Fazeli, A., Vardy, A., Cioffi, J., & Goldsmith, A. (2022). Parallelism versus latency in simplified successive-cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/TWC.2021.3125626
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[45]
2022 | Journal Article | IST-REx-ID: 12538 | OA
Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., & Rini, S. (2022). Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop. Mumbai, India: IEEE. https://doi.org/10.1109/ITW54588.2022.9965870
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[44]
2022 | Conference Paper | IST-REx-ID: 12537 | OA
Bombari, S., Amani, M. H., & Mondelli, M. (2022). Memorization and optimization in deep neural networks with minimum over-parameterization. In 36th Conference on Neural Information Processing Systems (Vol. 35, pp. 7628–7640). Curran Associates.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[43]
2022 | Journal Article | IST-REx-ID: 12480 | OA
Mondelli, M., & Venkataramanan, R. (2022). Approximate message passing with spectral initialization for generalized linear models. Journal of Statistical Mechanics: Theory and Experiment. IOP Publishing. https://doi.org/10.1088/1742-5468/ac9828
[Published Version] View | Files available | DOI | WoS
 
[42]
2021 | Conference Paper | IST-REx-ID: 10595 | OA
Nguyen, Q., Mondelli, M., & Montufar, G. F. (2021). Tight bounds on the smallest eigenvalue of the neural tangent kernel for deep ReLU networks. In M. Meila & T. Zhang (Eds.), Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[41]
2021 | Conference Paper | IST-REx-ID: 10599 | OA
Hashemi, S. A., Mondelli, M., Cioffi, J., & Goldsmith, A. (2021). Successive syndrome-check decoding of polar codes. In Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers (Vol. 2021–October, pp. 943–947). Virtual, Pacific Grove, CA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IEEECONF53345.2021.9723394
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[40]
2021 | Conference Paper | IST-REx-ID: 13146 | OA
Nguyen, Q., Mondelli, M., & Montufar, G. (2021). Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In Proceedings of the 38th International Conference on Machine Learning (Vol. 139, pp. 8119–8129). Virtual: ML Research Press.
[Published Version] View | Files available | arXiv
 
[39]
2021 | Journal Article | IST-REx-ID: 9047 | OA
Mondelli, M., Hashemi, S. A., Cioffi, J. M., & Goldsmith, A. (2021). Sublinear latency for simplified successive cancellation decoding of polar codes. IEEE Transactions on Wireless Communications. IEEE. https://doi.org/10.1109/TWC.2020.3022922
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[38]
2021 | Conference Paper | IST-REx-ID: 10053 | OA
Hashemi, S. A., Mondelli, M., Fazeli, A., Vardy, A., Cioffi, J., & Goldsmith, A. (2021). Parallelism versus latency in simplified successive-cancellation decoding of polar codes. In 2021 IEEE International Symposium on Information Theory (pp. 2369–2374). Melbourne, Australia: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISIT45174.2021.9518153
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS | arXiv
 
[37]
2021 | Conference Paper | IST-REx-ID: 10597 | OA
Fathollahi, D., Farsad, N., Hashemi, S. A., & Mondelli, M. (2021). Sparse multi-decoder recursive projection aggregation for Reed-Muller codes. In 2021 IEEE International Symposium on Information Theory (pp. 1082–1087). Virtual, Melbourne, Australia: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/isit45174.2021.9517887
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[36]
2021 | Journal Article | IST-REx-ID: 10211 | OA
Mondelli, M., Thrampoulidis, C., & Venkataramanan, R. (2021). Optimal combination of linear and spectral estimators for generalized linear models. Foundations of Computational Mathematics. Springer. https://doi.org/10.1007/s10208-021-09531-x
[Published Version] View | Files available | DOI | WoS | arXiv
 
[35]
2021 | Conference Paper | IST-REx-ID: 10593 | OA
Mondelli, M., & Venkataramanan, R. (2021). PCA initialization for approximate message passing in rotationally invariant models. In 35th Conference on Neural Information Processing Systems (Vol. 35, pp. 29616–29629). Virtual: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[34]
2021 | Conference Paper | IST-REx-ID: 10594 | OA
Nguyen, Q., Bréchet, P., & Mondelli, M. (2021). When are solutions connected in deep networks? In 35th Conference on Neural Information Processing Systems (Vol. 35). Virtual: Neural Information Processing Systems Foundation.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[33]
2021 | Conference Paper | IST-REx-ID: 10598 | OA
Mondelli, M., & Venkataramanan, R. (2021). Approximate message passing with spectral initialization for generalized linear models. In A. Banerjee & K. Fukumizu (Eds.), Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (Vol. 130, pp. 397–405). Virtual, San Diego, CA, United States: ML Research Press.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[32]
2021 | Journal Article | IST-REx-ID: 9002
Fazeli, A., Hassani, H., Mondelli, M., & Vardy, A. (2021). Binary linear codes with optimal scaling: Polar codes with large kernels. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/TIT.2020.3038806
[Preprint] View | Files available | DOI | arXiv
 
[31]
2020 | Conference Paper | IST-REx-ID: 9221 | OA
Nguyen, Q., & Mondelli, M. (2020). Global convergence of deep networks with one wide layer followed by pyramidal topology. In 34th Conference on Neural Information Processing Systems (Vol. 33, pp. 11961–11972). Vancouver, Canada: Curran Associates.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[30]
2020 | Conference Paper | IST-REx-ID: 8536 | OA
Mondelli, M., Hashemi, S. A., Cioffi, J., & Goldsmith, A. (2020). Simplified successive cancellation decoding of polar codes has sublinear latency. In IEEE International Symposium on Information Theory - Proceedings (Vol. 2020–June). Los Angeles, CA, United States: IEEE. https://doi.org/10.1109/ISIT44484.2020.9174141
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[29]
2020 | Conference Paper | IST-REx-ID: 9198 | OA
Shevchenko, A., & Mondelli, M. (2020). Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 8773–8784). ML Research Press.
[Published Version] View | Files available | arXiv
 
[28]
2020 | Journal Article | IST-REx-ID: 6748 | OA
Javanmard, A., Mondelli, M., & Montanari, A. (2020). Analysis of a two-layer neural network via displacement convexity. Annals of Statistics. Institute of Mathematical Statistics. https://doi.org/10.1214/20-AOS1945
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
[27]
2019 | Journal Article | IST-REx-ID: 6662 | OA
Mondelli, M., & Montanari, A. (2019). Fundamental limits of weak recovery with applications to phase retrieval. Foundations of Computational Mathematics. Springer. https://doi.org/10.1007/s10208-018-9395-y
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[26]
2019 | Journal Article | IST-REx-ID: 6663 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2019). Construction of polar codes with sublinear complexity. IEEE. IEEE. https://doi.org/10.1109/tit.2018.2889667
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[25]
2019 | Conference Paper | IST-REx-ID: 6747 | OA
Mondelli, M., & Montanari, A. (2019). On the connection between learning two-layers neural networks and tensor  decomposition. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (Vol. 89, pp. 1051–1060). Naha, Okinawa, Japan: Proceedings of Machine Learning Research.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[24]
2019 | Journal Article | IST-REx-ID: 6750 | OA
Hashemi, S. A., Condo, C., Mondelli, M., & Gross, W. J. (2019). Rate-flexible fast polar decoders. IEEE Transactions on Signal Processing. IEEE. https://doi.org/10.1109/TSP.2019.2944738
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[23]
2019 | Journal Article | IST-REx-ID: 7007 | OA
Mondelli, M., Hassani, S. H., & Urbanke, R. (2019). A new coding paradigm for the primitive relay channel. Algorithms. MDPI. https://doi.org/10.3390/a12100218
[Published Version] View | Files available | DOI | arXiv
 
[22]
2018 | Conference Paper | IST-REx-ID: 6664 | OA
Hashemi, S. A., Doan, N., Mondelli, M., & Gross, W. (2018). Decoding Reed-Muller and polar codes by successive factor graph permutations. In 2018 IEEE 10th International Symposium on Turbo Codes & Iterative Information Processing (pp. 1–5). Hong Kong, China: IEEE. https://doi.org/10.1109/istc.2018.8625281
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[21]
2018 | Journal Article | IST-REx-ID: 6674
Hashemi, S. A., Mondelli, M., Hassani, S. H., Condo, C., Urbanke, R. L., & Gross, W. J. (2018). Decoder partitioning: Towards practical list decoding of polar codes. IEEE Transactions on Communications. IEEE. https://doi.org/10.1109/tcomm.2018.2832207
View | DOI
 
[20]
2018 | Conference Paper | IST-REx-ID: 6728 | OA
Doan, N., Hashemi, S. A., Mondelli, M., & Gross, W. J. (2018). On the decoding of polar codes on permuted factor graphs. In 2018 IEEE Global Communications Conference . Abu Dhabi, United Arab Emirates: IEEE. https://doi.org/10.1109/glocom.2018.8647308
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[19]
2018 | Journal Article | IST-REx-ID: 6678 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2018). How to achieve the capacity of asymmetric channels. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2018.2789885
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[18]
2018 | Conference Paper | IST-REx-ID: 6675 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2018). A new coding paradigm for the primitive relay channel. In 2018 IEEE International Symposium on Information Theory (pp. 351–355). Vail, CO, United States: IEEE. https://doi.org/10.1109/isit.2018.8437479
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[17]
2018 | Conference Paper | IST-REx-ID: 6665 | OA
Fazeli, A., Hassani, H., Mondelli, M., & Vardy, A. (2018). Binary linear codes with optimal scaling: Polar codes with large kernels. In 2018 IEEE Information Theory Workshop (pp. 1–5). Guangzhou, China: IEEE. https://doi.org/10.1109/itw.2018.8613428
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[16]
2017 | Conference Paper | IST-REx-ID: 6679 | OA
Hashemi, S. A., Mondelli, M., Hassani, H., Urbanke, R., & Gross, W. (2017). Partitioned list decoding of polar codes: Analysis and improvement of finite length performance. In 2017 IEEE Global Communications Conference (pp. 1–7). Singapore, Singapore: IEEE. https://doi.org/10.1109/glocom.2017.8254940
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[15]
2017 | Conference Paper | IST-REx-ID: 6729 | OA
Mondelli, M., Hassani, S. H., & Urbanke, R. (2017). Construction of polar codes with sublinear complexity. In 2017 IEEE International Symposium on Information Theory (pp. 1853–1857). Aachen, Germany: IEEE. https://doi.org/10.1109/isit.2017.8006850
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[14]
2017 | Journal Article | IST-REx-ID: 6730 | OA
Kudekar, S., Kumar, S., Mondelli, M., Pfister, H. D., Sasoglu, E., & Urbanke, R. L. (2017). Reed–Muller codes achieve capacity on erasure channels. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2017.2673829
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[13]
2017 | Conference Paper | IST-REx-ID: 6731 | OA
Mondelli, M., Hassani, H., Maric, I., Hui, D., & Hong, S.-N. (2017). Capacity-achieving rate-compatible polar codes for general channels. In 2017 IEEE Wireless Communications and Networking Conference Workshops . San Francisco, CA, USA: IEEE. https://doi.org/10.1109/wcncw.2017.7919107
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[12]
2016 | Journal Article | IST-REx-ID: 6732 | OA
Mondelli, M., Hassani, S. H., & Urbanke, R. L. (2016). Unified scaling of polar codes: Error exponent, scaling exponent, moderate deviations, and error floors. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2016.2616117
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[11]
2016 | Conference Paper | IST-REx-ID: 6733 | OA
Kudekar, S., Kumar, S., Mondelli, M., Pfister, H. D., & Urbankez, R. (2016). Comparing the bit-MAP and block-MAP decoding thresholds of Reed-Muller codes on BMS channels. In 2016 IEEE International Symposium on Information Theory (pp. 1755–1759). Barcelona, Spain: IEEE. https://doi.org/10.1109/isit.2016.7541600
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[10]
2016 | Conference Paper | IST-REx-ID: 6770
Mondelli, M., Kudekar, S., Kumar, S., Pfister, H. D., Şaşoğlu, E., & Urbanke, R. (2016). Reed-Muller codes: Thresholds and weight distribution. In 24th International Zurich Seminar on Communications (p. 50). Zurich, Switzerland: ETH Zürich. https://doi.org/10.3929/ETHZ-A-010646484
View | DOI
 
[9]
2015 | Journal Article | IST-REx-ID: 6737 | OA
Mondelli, M., Hassani, H., Sason, I., & Urbanke, R. (2015). Achieving Marton’s region for broadcast channels using polar codes. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2014.2368555
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[8]
2015 | Journal Article | IST-REx-ID: 6736 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2015). Scaling exponent of list decoders with applications to polar codes. IEEE Transactions on Information Theory. IEEE. https://doi.org/10.1109/tit.2015.2453315
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[7]
2014 | Conference Paper | IST-REx-ID: 6740 | OA
Mondelli, M., Urbanke, R., & Hassani, H. (2014). How to achieve the capacity of asymmetric channels. In 52nd Annual Allerton Conference on Communication, Control, and Computing (pp. 789–796). Monticello, IL, United States: IEEE. https://doi.org/10.1109/allerton.2014.7028535
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
[6]
2014 | Journal Article | IST-REx-ID: 6739 | OA
Mondelli, M., Hassani, H., & Urbanke, R. (2014). From polar to Reed-Muller codes: A technique to improve the finite-length performance. IEEE Transactions on Communications. IEEE. https://doi.org/10.1109/tcomm.2014.2345069
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[5]
2014 | Journal Article | IST-REx-ID: 6744
Mondelli, M., Zhou, Q., Lottici, V., & Ma, X. (2014). Joint power allocation and path selection for multi-hop noncoherent decode and forward UWB communications. IEEE Transactions on Wireless Communications. IEEE. https://doi.org/10.1109/twc.2014.020914.130669
View | DOI
 
[4]
2013 | Journal Article | IST-REx-ID: 6768 | OA
Mondelli, M. (2013). A finite difference scheme for the stack filter simulating the MCM. Image Processing On Line. Image Processing On Line. https://doi.org/10.5201/ipol.2013.53
[Published Version] View | Files available | DOI
 
[3]
2012 | Conference Paper | IST-REx-ID: 6746
Mondelli, M., Zhou, Q., Ma, X., & Lottici, V. (2012). A cooperative approach for amplify-and-forward differential transmitted reference IR-UWB relay systems. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2905–2908). Kyoto, Japan: IEEE. https://doi.org/10.1109/icassp.2012.6288524
View | DOI
 
[2]
2011 | Journal Article | IST-REx-ID: 6749 | OA
Mondelli, M., & Ciomaga, A. (2011). Finite difference schemes for MCM and AMSS. Image Processing On Line. IPOL Image Processing On Line. https://doi.org/10.5201/ipol.2011.cm_fds
[Published Version] View | Files available | DOI
 
[1]
2011 | Conference Paper | IST-REx-ID: 6767
Mondelli, M., & Ciomaga, A. (2011). On finite difference schemes for curvature motions. In Proceedings of the International Student Conference on Pure and Applied Mathematics (pp. 137–156). Iasi, Romania: Editura Universitãtii „Alexandru Ioan Cuza” Iasi. https://doi.org/10.13140/2.1.1862.4646
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