DOI,IST REx ID,Research Group,Title of publication
null,14924,MaMo,"Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence"
null,14921,"MaMo,ChLa",Deep neural collapse is provably optimal for the deep unconstrained features model
10.1109/ITW55543.2023.10160238,13321,MaMo,Approximate message passing for multi-layer estimation in rotationally invariant models
10.1109/tit.2023.3257239,14751,MaMo,Zero-error communication over adversarial MACs
10.1109/TIT.2023.3292219,13269,MaMo,Codes for the Z-channel
null,14459,"MaMo,DaAl","Fundamental limits of two-layer autoencoders, and achieving them with gradient methods"
10.1109/TIT.2022.3189542,11639,MaMo,List decoding random Euclidean codes and Infinite constellations
10.1109/ISIT50566.2022.9834709,12011,MaMo,The capacity of causal adversarial channels
10.1109/ISIT50566.2022.9834711,12012,MaMo,Heterogeneous differential privacy via graphs
10.1109/ISIT50566.2022.9834850,12013,MaMo,On the capacity of additive AVCs with feedback
10.1109/ISIT50566.2022.9834512,12014,MaMo,List-decodability of Poisson Point Processes
10.1109/ISIT50566.2022.9834443,12015,MaMo,Lower bounds for multiple packing
10.1109/ISIT50566.2022.9834712,12016,MaMo,Polar coded computing: The role of the scaling exponent
10.1088/1742-5468/ac9828,12480,MaMo,Approximate message passing with spectral initialization for generalized linear models
null,12537,MaMo,Memorization and optimization in deep neural networks with minimum over-parameterization
10.1109/ITW54588.2022.9965870,12538,MaMo,Sharp asymptotics on the compression of two-layer neural networks
null,12540,MaMo,Estimation in rotationally invariant generalized linear models via approximate message passing
10.48550/arXiv.2203.16701,12860,"GradSch,MaMo",Towards differential relational privacy and its use in question answering
null,17086,MaMo,Mean estimation in high-dimensional binary Markov Gaussian mixture models
10.1007/s10208-021-09531-x,10211,MaMo,Optimal combination of linear and spectral estimators for generalized linear models