The approximate capacity of the many-to-one and one-to-many Gaussian interference channels G Bresler, A Parekh, DNC Tse Information Theory, IEEE Transactions on 56 (9), 4566-4592, 2010 | 374 | 2010 |
The two‐user Gaussian interference channel: a deterministic view G Bresler, D Tse European transactions on telecommunications 19 (4), 333-354, 2008 | 270 | 2008 |
Feasibility of Interference Alignment for the MIMO Interference Channel G Bresler, D Cartwright, D Tse Information Theory, IEEE Transactions on 60 (9), 5573-5586, 2014 | 222* | 2014 |
Efficiently learning Ising models on arbitrary graphs G Bresler Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015 | 220 | 2015 |
Mixing time of exponential random graphs S Bhamidi, G Bresler, A Sly 2008 49th Annual IEEE Symposium on Foundations of Computer Science, 803-812, 2008 | 207 | 2008 |
Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms G Bresler, E Mossel, A Sly SIAM Journal on Computing 42 (2), 563-578, 2013 | 179* | 2013 |
Information Theory of DNA Shotgun Sequencing A Motahari, G Bresler, D Tse IEEE Transactions on Information Theory, 1-1, 2013 | 148 | 2013 |
Reducibility and computational lower bounds for problems with planted sparse structure M Brennan, G Bresler, W Huleihel Conference On Learning Theory, 48-166, 2018 | 120 | 2018 |
Reducibility and statistical-computational gaps from secret leakage M Brennan, G Bresler Conference on Learning Theory, 648-847, 2020 | 104* | 2020 |
Optimal assembly for high throughput shotgun sequencing G Bresler, M Bresler, D Tse BMC bioinformatics 14 (5), 1-13, 2013 | 98 | 2013 |
A Latent Source Model for Online Collaborative Filtering G Bresler, GH Chen, D Shah Advances in Neural Information Processing Systems, 3347-3355, 2014 | 86 | 2014 |
The staircase property: How hierarchical structure can guide deep learning E Abbe, E Boix Adsera, M Brennan, G Bresler, D Nagaraj Advances in Neural Information Processing Systems 34, 2021 | 82* | 2021 |
Statistical query algorithms and low-degree tests are almost equivalent M Brennan, G Bresler, SB Hopkins, J Li, T Schramm Conference on Learning Theory, 2021 | 65 | 2021 |
Optimal average-case reductions to sparse pca: From weak assumptions to strong hardness M Brennan, G Bresler Conference on Learning Theory, 469-470, 2019 | 64 | 2019 |
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms D Nagaraj, X Wu, G Bresler, P Jain, P Netrapalli Advances in Neural Information Processing Systems 33, 2020 | 58 | 2020 |
Learning a tree-structured Ising model in order to make predictions G Bresler, M Karzand The Annals of Statistics 48 (2), 713-737, 2020 | 57 | 2020 |
3 user interference channel: Degrees of freedom as a function of channel diversity G Bresler, DNC Tse Communication, Control, and Computing, 2009. Allerton 2009. 47th Annual …, 2009 | 50* | 2009 |
The algorithmic phase transition of random k-sat for low degree polynomials G Bresler, B Huang 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 47 | 2022 |
Structure learning of antiferromagnetic Ising models G Bresler, D Gamarnik, D Shah Advances in Neural Information Processing Systems, 2852-2860, 2014 | 45 | 2014 |
Sample Efficient Active Learning of Causal Trees K Greenewald, D Katz, K Shanmugam, S Magliacane, M Kocaoglu, ... Advances in Neural Information Processing Systems, 14279-14289, 2019 | 44 | 2019 |