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2020 – today
- 2024
[j23]Amedeo Roberto Esposito
, Marco Mondelli:
Concentration Without Independence via Information Measures. IEEE Trans. Inf. Theory 70(6): 3823-3839 (2024)- [j22]Francesco Pedrotti, Jan Maas, Marco Mondelli:
Improved Convergence of Score-Based Diffusion Models via Prediction-Correction. Trans. Mach. Learn. Res. 2024 (2024) - [c48]Amedeo Roberto Esposito, Marco Mondelli:
Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo (Extended Abstract). COLT 2024: 1643-1645 - [c47]Yihan Zhang, Hong Chang Ji, Ramji Venkataramanan, Marco Mondelli:
Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing (Extended Abstract). COLT 2024: 5224-5230 - [c46]Al Depope, Marco Mondelli, Matthew R. Robinson:
Inference of Genetic Effects via Approximate Message Passing. ICASSP 2024: 13151-13155 - [c45]Simone Bombari, Marco Mondelli:
How Spurious Features are Memorized: Precise Analysis for Random and NTK Features. ICML 2024 - [c44]Simone Bombari, Marco Mondelli:
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features. ICML 2024 - [c43]Kevin Kögler, Aleksandr Shevchenko, Hamed Hassani, Marco Mondelli:
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth. ICML 2024 - [c42]Yihan Zhang, Marco Mondelli:
Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods. NeurIPS 2024 - [c41]Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Misha Belkin:
Average gradient outer product as a mechanism for deep neural collapse. NeurIPS 2024 - [c40]Peter Súkeník, Christoph H. Lampert, Marco Mondelli:
Neural collapse vs. low-rank bias: Is deep neural collapse really optimal? NeurIPS 2024 - [i60]Simone Bombari, Marco Mondelli:
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features. CoRR abs/2402.02969 (2024) - [i59]Kevin Kögler, Alexander Shevchenko, Hamed Hassani, Marco Mondelli:
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth. CoRR abs/2402.05013 (2024) - [i58]Amedeo Roberto Esposito, Marco Mondelli:
Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo. CoRR abs/2402.11200 (2024) - [i57]Daniel Beaglehole, Peter Súkeník, Marco Mondelli, Mikhail Belkin:
Average gradient outer product as a mechanism for deep neural collapse. CoRR abs/2402.13728 (2024) - [i56]Yihan Zhang, Marco Mondelli:
Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods. CoRR abs/2405.13912 (2024) - [i55]Peter Súkeník, Marco Mondelli, Christoph Lampert:
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal? CoRR abs/2405.14468 (2024) - [i54]Jean Barbier, Francesco Camilli, Marco Mondelli, Yizhou Xu:
Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise. CoRR abs/2405.20993 (2024) - [i53]Arthur Jacot, Peter Súkeník, Zihan Wang, Marco Mondelli:
Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse. CoRR abs/2410.04887 (2024) - [i52]Simone Bombari, Marco Mondelli:
Privacy for Free in the Over-Parameterized Regime. CoRR abs/2410.14787 (2024) - [i51]Muhammed Emrullah Ildiz, Halil Alperen Gozeten, Ege Onur Taga, Marco Mondelli, Samet Oymak:
High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws. CoRR abs/2410.18837 (2024) - 2023
- [j21]Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli:
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. Trans. Mach. Learn. Res. 2023 (2023) - [c39]Simone Bombari, Shayan Kiyani, Marco Mondelli:
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels. ICML 2023: 2738-2776 - [c38]Aleksandr Shevchenko, Kevin Kögler, Hamed Hassani, Marco Mondelli:
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods. ICML 2023: 31151-31209 - [c37]Amedeo Roberto Esposito, Marco Mondelli:
Concentration without Independence via Information Measures. ISIT 2023: 400-405 - [c36]Teng Fu, Yuhao Liu, Jean Barbier, Marco Mondelli, Shansuo Liang, TianQi Hou:
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. ISIT 2023: 1178-1183 - [c35]Yizhou Xu, TianQi Hou, Shansuo Liang, Marco Mondelli:
Approximate Message Passing for Multi-Layer Estimation in Rotationally Invariant Models. ITW 2023: 294-298 - [c34]Peter Súkeník, Marco Mondelli, Christoph H. Lampert:
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model. NeurIPS 2023 - [i50]Simone Bombari, Shayan Kiyani, Marco Mondelli:
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels. CoRR abs/2302.01629 (2023) - [i49]Teng Fu, Yuhao Liu, Jean Barbier, Marco Mondelli, Shansuo Liang, TianQi Hou:
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise. CoRR abs/2302.03306 (2023) - [i48]Amedeo Roberto Esposito, Marco Mondelli:
Concentration without Independence via Information Measures. CoRR abs/2303.07245 (2023) - [i47]Simone Bombari, Marco Mondelli:
Stability, Generalization and Privacy: Precise Analysis for Random and NTK Features. CoRR abs/2305.12100 (2023) - [i46]Peter Súkeník, Marco Mondelli, Christoph Lampert:
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model. CoRR abs/2305.13165 (2023) - [i45]Francesco Pedrotti, Jan Maas, Marco Mondelli:
Improved Convergence of Score-Based Diffusion Models via Prediction-Correction. CoRR abs/2305.14164 (2023) - [i44]Yihan Zhang, Hong Chang Ji, Ramji Venkataramanan, Marco Mondelli:
Spectral Estimators for Structured Generalized Linear Models via Approximate Message Passing. CoRR abs/2308.14507 (2023) - 2022
- [j20]Marco Mondelli, Christos Thrampoulidis, Ramji Venkataramanan:
Optimal Combination of Linear and Spectral Estimators for Generalized Linear Models. Found. Comput. Math. 22(5): 1513-1566 (2022) - [j19]Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli:
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. J. Mach. Learn. Res. 23: 130:1-130:55 (2022) - [j18]Nghia Doan, Seyyed Ali Hashemi, Marco Mondelli, Warren J. Gross:
Decoding Reed-Muller Codes With Successive Codeword Permutations. IEEE Trans. Commun. 70(11): 7134-7145 (2022) - [j17]Seyyed Ali Hashemi, Marco Mondelli, Arman Fazeli, Alexander Vardy, John M. Cioffi, Andrea Goldsmith:
Parallelism Versus Latency in Simplified Successive-Cancellation Decoding of Polar Codes. IEEE Trans. Wirel. Commun. 21(6): 3909-3920 (2022) - [c33]Ramji Venkataramanan, Kevin Kögler, Marco Mondelli:
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing. ICML 2022: 22120-22144 - [c32]Dorsa Fathollahi, Marco Mondelli:
Polar Coded Computing: The Role of the Scaling Exponent. ISIT 2022: 2154-2159 - [c31]Mohammad Hossein Amani, Simone Bombari, Marco Mondelli, Rattana Pukdee, Stefano Rini:
Sharp asymptotics on the compression of two-layer neural networks. ITW 2022: 588-593 - [c30]Jean Barbier, TianQi Hou, Marco Mondelli, Manuel Sáenz:
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation? NeurIPS 2022 - [c29]Simone Bombari, Mohammad Hossein Amani, Marco Mondelli:
Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization. NeurIPS 2022 - [i43]Dorsa Fathollahi, Marco Mondelli:
Polar Coded Computing: The Role of the Scaling Exponent. CoRR abs/2201.10082 (2022) - [i42]Mohammad Hossein Amani, Simone Bombari, Marco Mondelli, Rattana Pukdee, Stefano Rini:
Sharp asymptotics on the compression of two-layer neural networks. CoRR abs/2205.08199 (2022) - [i41]Jean Barbier, Tianqi Hou, Marco Mondelli, Manuel Sáenz:
The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation? CoRR abs/2205.10009 (2022) - [i40]Simone Bombari, Mohammad Hossein Amani, Marco Mondelli:
Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization. CoRR abs/2205.10217 (2022) - [i39]Jean Barbier, Francesco Camilli, Marco Mondelli, Manuel Sáenz:
Bayes-optimal limits in structured PCA, and how to reach them. CoRR abs/2210.01237 (2022) - [i38]Diyuan Wu, Vyacheslav Kungurtsev, Marco Mondelli:
Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence. CoRR abs/2210.06819 (2022) - [i37]Massimo Fornasier, Timo Klock, Marco Mondelli, Michael Rauchensteiner:
Finite Sample Identification of Wide Shallow Neural Networks with Biases. CoRR abs/2211.04589 (2022) - [i36]Yihan Zhang, Marco Mondelli, Ramji Venkataramanan:
Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models. CoRR abs/2211.11368 (2022) - [i35]Yizhou Xu, Tianqi Hou, Shansuo Liang, Marco Mondelli:
Approximate Message Passing for Multi-Layer Estimation in Rotationally Invariant Models. CoRR abs/2212.01572 (2022) - [i34]Alexander Shevchenko, Kevin Kögler, Hamed Hassani, Marco Mondelli:
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods. CoRR abs/2212.13468 (2022) - 2021
- [j16]Arman Fazeli, Hamed Hassani, Marco Mondelli, Alexander Vardy:
Binary Linear Codes With Optimal Scaling: Polar Codes With Large Kernels. IEEE Trans. Inf. Theory 67(9): 5693-5710 (2021) - [j15]Marco Mondelli, Seyyed Ali Hashemi, John M. Cioffi, Andrea Goldsmith:
Sublinear Latency for Simplified Successive Cancellation Decoding of Polar Codes. IEEE Trans. Wirel. Commun. 20(1): 18-27 (2021) - [c28]Seyyed Ali Hashemi, Marco Mondelli, John M. Cioffi, Andrea Goldsmith:
Successive Syndrome-Check Decoding of Polar Codes. ACSCC 2021: 943-947 - [c27]Marco Mondelli, Ramji Venkataramanan:
Approximate Message Passing with Spectral Initialization for Generalized Linear Models. AISTATS 2021: 397-405 - [c26]Quynh Nguyen, Marco Mondelli, Guido F. Montúfar:
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks. ICML 2021: 8119-8129 - [c25]Dorsa Fathollahi, Nariman Farsad, Seyyed Ali Hashemi, Marco Mondelli:
Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes. ISIT 2021: 1082-1087 - [c24]Seyyed Ali Hashemi, Marco Mondelli, Arman Fazeli, Alexander Vardy, John M. Cioffi, Andrea Goldsmith:
Parallelism versus Latency in Simplified Successive-Cancellation Decoding of Polar Codes. ISIT 2021: 2369-2374 - [c23]Quynh Nguyen, Pierre Bréchet, Marco Mondelli:
When Are Solutions Connected in Deep Networks? NeurIPS 2021: 20956-20969 - [c22]Marco Mondelli, Ramji Venkataramanan:
PCA Initialization for Approximate Message Passing in Rotationally Invariant Models. NeurIPS 2021: 29616-29629 - [i33]Quynh Nguyen, Pierre Bréchet, Marco Mondelli:
On Connectivity of Solutions in Deep Learning: The Role of Over-parameterization and Feature Quality. CoRR abs/2102.09671 (2021) - [i32]Marco Mondelli, Ramji Venkataramanan:
PCA Initialization for Approximate Message Passing in Rotationally Invariant Models. CoRR abs/2106.02356 (2021) - [i31]Nghia Doan, Seyyed Ali Hashemi, Marco Mondelli, Warren J. Gross:
Decoding Reed-Muller Codes with Successive Factor-Graph Permutations. CoRR abs/2109.02122 (2021) - [i30]Alexander Shevchenko, Vyacheslav Kungurtsev, Marco Mondelli:
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks. CoRR abs/2111.02278 (2021) - [i29]Seyyed Ali Hashemi, Marco Mondelli, John M. Cioffi, Andrea Goldsmith:
Successive Syndrome-Check Decoding of Polar Codes. CoRR abs/2112.00057 (2021) - [i28]Ramji Venkataramanan, Kevin Kögler, Marco Mondelli:
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing. CoRR abs/2112.04330 (2021) - 2020
- [c21]Alexander Shevchenko, Marco Mondelli:
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks. ICML 2020: 8773-8784 - [c20]Marco Mondelli, Seyyed Ali Hashemi, John M. Cioffi, Andrea Goldsmith:
Simplified Successive Cancellation Decoding of Polar Codes Has Sublinear Latency. ISIT 2020: 401-406 - [c19]Quynh Nguyen, Marco Mondelli:
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology. NeurIPS 2020 - [i27]Quynh Nguyen, Marco Mondelli:
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology. CoRR abs/2002.07867 (2020) - [i26]Marco Mondelli, Christos Thrampoulidis, Ramji Venkataramanan:
Optimal Combination of Linear and Spectral Estimators for Generalized Linear Models. CoRR abs/2008.03326 (2020) - [i25]Marco Mondelli, Ramji Venkataramanan:
Approximate Message Passing with Spectral Initialization for Generalized Linear Models. CoRR abs/2010.03460 (2020) - [i24]Dorsa Fathollahi, Nariman Farsad, Seyyed Ali Hashemi, Marco Mondelli:
Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes. CoRR abs/2011.12882 (2020) - [i23]Quynh Nguyen, Marco Mondelli, Guido Montúfar:
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks. CoRR abs/2012.11654 (2020) - [i22]Seyyed Ali Hashemi, Marco Mondelli, Arman Fazeli, Alexander Vardy, John M. Cioffi, Andrea Goldsmith:
Parallelism versus Latency in Simplified Successive-Cancellation Decoding of Polar Codes. CoRR abs/2012.13378 (2020)
2010 – 2019
- 2019
- [j14]Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke:
A New Coding Paradigm for the Primitive Relay Channel. Algorithms 12(10): 218 (2019) - [j13]Marco Mondelli, Andrea Montanari:
Fundamental Limits of Weak Recovery with Applications to Phase Retrieval. Found. Comput. Math. 19(3): 703-773 (2019) - [j12]Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke:
Construction of Polar Codes With Sublinear Complexity. IEEE Trans. Inf. Theory 65(5): 2782-2791 (2019) - [j11]Seyyed Ali Hashemi, Carlo Condo, Marco Mondelli, Warren J. Gross:
Rate-Flexible Fast Polar Decoders. IEEE Trans. Signal Process. 67(22): 5689-5701 (2019) - [c18]Marco Mondelli, Andrea Montanari:
On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition. AISTATS 2019: 1051-1060 - [c17]Seyyed Ali Hashemi, Carlo Condo, Marco Mondelli, Warren J. Gross:
Rate-Flexible Fast Polar Decoders. ITW 2019: 1-5 - [i21]Adel Javanmard, Marco Mondelli, Andrea Montanari:
Analysis of a Two-Layer Neural Network via Displacement Convexity. CoRR abs/1901.01375 (2019) - [i20]Seyyed Ali Hashemi, Carlo Condo, Marco Mondelli, Warren J. Gross:
Rate-Flexible Fast Polar Decoders. CoRR abs/1903.09203 (2019) - [i19]Marco Mondelli, Seyyed Ali Hashemi, John M. Cioffi, Andrea Goldsmith:
Sublinear Latency for Simplified Successive Cancellation Decoding of Polar Codes. CoRR abs/1909.04892 (2019) - [i18]Alexander Shevchenko, Marco Mondelli:
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks. CoRR abs/1912.10095 (2019) - 2018
- [j10]Seyyed Ali Hashemi, Marco Mondelli, S. Hamed Hassani, Carlo Condo, Rüdiger L. Urbanke, Warren J. Gross:
Decoder Partitioning: Towards Practical List Decoding of Polar Codes. IEEE Trans. Commun. 66(9): 3749-3759 (2018) - [j9]Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke:
How to Achieve the Capacity of Asymmetric Channels. IEEE Trans. Inf. Theory 64(5): 3371-3393 (2018) - [c16]Marco Mondelli, Andrea Montanari:
Fundamental Limits of Weak Recovery with Applications to Phase Retrieval. COLT 2018: 1445-1450 - [c15]Nghia Doan, Seyyed Ali Hashemi, Marco Mondelli, Warren J. Gross:
On the Decoding of Polar Codes on Permuted Factor Graphs. GLOBECOM 2018: 1-6 - [c14]Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke:
A New Coding Paradigm for the Primitive Relay Channel. ISIT 2018: 351-355 - [c13]Seyyed Ali Hashemi, Nghia Doan, Marco Mondelli, Warren J. Gross:
Decoding Reed-Muller and Polar Codes by Successive Factor Graph Permutations. ISTC 2018: 1-5 - [c12]Arman Fazeli, S. Hamed Hassani, Marco Mondelli, Alexander Vardy:
Binary Linear Codes with Optimal Scaling: Polar Codes with Large Kernels. ITW 2018: 1-5 - [i17]Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke:
A New Coding Paradigm for the Primitive Relay Channel. CoRR abs/1801.03153 (2018) - [i16]Marco Mondelli, Andrea Montanari:
On the Connection Between Learning Two-Layers Neural Networks and Tensor Decomposition. CoRR abs/1802.07301 (2018) - [i15]Nghia Doan, Seyyed Ali Hashemi, Marco Mondelli, Warren J. Gross:
On the Decoding of Polar Codes on Permuted Factor Graphs. CoRR abs/1806.11195 (2018) - [i14]Seyyed Ali Hashemi, Nghia Doan, Marco Mondelli, Warren J. Gross:
Decoding Reed-Muller and Polar Codes by Successive Factor Graph Permutations. CoRR abs/1807.03912 (2018) - 2017
- [j8]Shrinivas Kudekar, Santhosh Kumar, Marco Mondelli, Henry D. Pfister, Eren Sasoglu, Rüdiger L. Urbanke:
Reed-Muller Codes Achieve Capacity on Erasure Channels. IEEE Trans. Inf. Theory 63(7): 4298-4316 (2017) - [c11]Seyyed Ali Hashemi, Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke, Warren J. Gross:
Partitioned List Decoding of Polar Codes: Analysis and Improvement of Finite Length Performance. GLOBECOM 2017: 1-7 - [c10]Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke:
Construction of polar codes with sublinear complexity. ISIT 2017: 1853-1857 - [c9]Marco Mondelli, S. Hamed Hassani, Ivana Maric, Dennis Hui, Song-Nam Hong:
Capacity-Achieving Rate-Compatible Polar Codes for General Channels. WCNC Workshops 2017: 1-6 - [i13]Seyyed Ali Hashemi, Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke, Warren J. Gross:
Partitioned List Decoding of Polar Codes: Analysis and Improvement of Finite Length Performance. CoRR abs/1705.05497 (2017) - [i12]Marco Mondelli, Andrea Montanari:
Fundamental Limits of Weak Recovery with Applications to Phase Retrieval. CoRR abs/1708.05932 (2017) - [i11]Arman Fazeli, S. Hamed Hassani, Marco Mondelli, Alexander Vardy:
Binary Linear Codes with Optimal Scaling and Quasi-Linear Complexity. CoRR abs/1711.01339 (2017) - 2016
- [b1]Marco Mondelli:
From Polar to Reed-Muller Codes - Unified Scaling, Non-standard Channels, and a Proven Conjecture. EPFL, Switzerland, 2016 - [j7]Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke:
Unified Scaling of Polar Codes: Error Exponent, Scaling Exponent, Moderate Deviations, and Error Floors. IEEE Trans. Inf. Theory 62(12): 6698-6712 (2016) - [c8]Shrinivas Kudekar, Santhosh Kumar, Marco Mondelli, Henry D. Pfister, Rüdiger L. Urbanke:
Comparing the bit-MAP and block-MAP decoding thresholds of reed-muller codes on BMS channels. ISIT 2016: 1755-1759 - [c7]Shrinivas Kudekar, Santhosh Kumar, Marco Mondelli, Henry D. Pfister, Eren Sasoglu, Rüdiger L. Urbanke:
Reed-Muller codes achieve capacity on erasure channels. STOC 2016: 658-669 - [i10]Shrinivas Kudekar, Santhosh Kumar, Marco Mondelli, Henry D. Pfister, Eren Sasoglu, Rüdiger L. Urbanke:
Reed-Muller Codes Achieve Capacity on Erasure Channels. CoRR abs/1601.04689 (2016) - [i9]Shrinivas Kudekar, Santhosh Kumar, Marco Mondelli, Henry D. Pfister, Rüdiger L. Urbanke:
Comparing the Bit-MAP and Block-MAP Decoding Thresholds of Reed-Muller Codes on BMS Channels. CoRR abs/1601.06048 (2016) - [i8]Marco Mondelli, S. Hamed Hassani, Ivana Maric, Dennis Hui, Song-Nam Hong:
Capacity-Achieving Rate-Compatible Polar Codes for General Channels. CoRR abs/1611.01199 (2016) - [i7]Marco Mondelli, S. Hamed Hassani, Rüdiger L. Urbanke:
Construction of Polar Codes with Sublinear Complexity. CoRR abs/1612.05295 (2016) - 2015
- [j6]Marco Mondelli, Seyed Hamed Hassani, Igal Sason, Rüdiger L. Urbanke:
Achieving Marton's Region for Broadcast Channels Using Polar Codes. IEEE Trans. Inf. Theory 61(2): 783-800 (2015) - [j5]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke:
Scaling Exponent of List Decoders With Applications to Polar Codes. IEEE Trans. Inf. Theory 61(9): 4838-4851 (2015) - [c6]Marco Mondelli, Rüdiger L. Urbanke, Seyed Hamed Hassani:
Unified scaling of polar codes: Error exponent, scaling exponent, moderate deviations, and error floors. ISIT 2015: 1422-1426 - [i6]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke:
Unified Scaling of Polar Codes: Error Exponent, Scaling Exponent, Moderate Deviations, and Error Floors. CoRR abs/1501.02444 (2015) - [i5]Shrinivas Kudekar, Marco Mondelli, Eren Sasoglu, Rüdiger L. Urbanke:
Reed-Muller Codes Achieve Capacity on the Binary Erasure Channel under MAP Decoding. CoRR abs/1505.05831 (2015) - 2014
- [j4]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke:
From Polar to Reed-Muller Codes: A Technique to Improve the Finite-Length Performance. IEEE Trans. Commun. 62(9): 3084-3091 (2014) - [j3]Marco Mondelli, Qi Zhou, Vincenzo Lottici, Xiaoli Ma:
Joint Power Allocation and Path Selection for Multi-Hop Noncoherent Decode and Forward UWB Communications. IEEE Trans. Wirel. Commun. 13(3): 1397-1409 (2014) - [c5]Marco Mondelli, Rüdiger L. Urbanke, Seyed Hamed Hassani:
How to achieve the capacity of asymmetric channels. Allerton 2014: 789-796 - [c4]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke:
From polar to Reed-Muller codes: A technique to improve the finite-length performance. ISIT 2014: 131-135 - [c3]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke, Igal Sason:
Achieving Marton's region for broadcast channels using polar codes. ISIT 2014: 306-310 - [i4]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke:
From Polar to Reed-Muller Codes: a Technique to Improve the Finite-Length Performance. CoRR abs/1401.3127 (2014) - [i3]Marco Mondelli, Seyed Hamed Hassani, Igal Sason, Rüdiger L. Urbanke:
Achieving the Superposition and Binning Regions for Broadcast Channels Using Polar Codes. CoRR abs/1401.6060 (2014) - [i2]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke:
How to Achieve the Capacity of Asymmetric Channels. CoRR abs/1406.7373 (2014) - 2013
- [j2]Marco Mondelli:
A Finite Difference Scheme for the Stack Filter Simulating the MCM. Image Process. Line 3: 68-111 (2013) - [c2]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke:
Scaling exponent of list decoders with applications to polar codes. ITW 2013: 1-5 - [i1]Marco Mondelli, Seyed Hamed Hassani, Rüdiger L. Urbanke:
Scaling Exponent of List Decoders with Applications to Polar Codes. CoRR abs/1304.5220 (2013) - 2012
- [c1]Marco Mondelli, Qi Zhou, Xiaoli Ma, Vincenzo Lottici:
A cooperative approach for amplify-and-forward differential transmitted reference IR-UWB relay systems. ICASSP 2012: 2905-2908 - 2011
- [j1]Marco Mondelli, Adina Ciomaga:
Finite Difference Schemes for MCM and AMSS. Image Process. Line 1 (2011)
Coauthor Index
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