| 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 | 359 | 2010 |
| The two‐user Gaussian interference channel: a deterministic view G Bresler, D Tse European transactions on telecommunications 19 (4), 333-354, 2008 | 263 | 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 | 212* | 2014 |
| 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 | 168 | 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 | 152* | 2013 |
| Efficiently learning Ising models on arbitrary graphs G Bresler Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015 | 139 | 2015 |
| Information Theory of DNA Shotgun Sequencing A Motahari, G Bresler, D Tse IEEE Transactions on Information Theory, 1-1, 2013 | 94* | 2013 |
| Optimal assembly for high throughput shotgun sequencing G Bresler, M Bresler, D Tse BMC bioinformatics 14 (5), 1-13, 2013 | 63 | 2013 |
| A Latent Source Model for Online Collaborative Filtering G Bresler, GH Chen, D Shah Advances in Neural Information Processing Systems, 3347-3355, 2014 | 60 | 2014 |
| Reducibility and computational lower bounds for problems with planted sparse structure M Brennan, G Bresler, W Huleihel Conference On Learning Theory, 48-166, 2018 | 54 | 2018 |
| 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 | 48* | 2009 |
| Structure learning of antiferromagnetic Ising models G Bresler, D Gamarnik, D Shah Advances in Neural Information Processing Systems, 2852-2860, 2014 | 33 | 2014 |
| Learning a tree-structured Ising model in order to make predictions G Bresler, M Karzand Annals of Statistics 48 (2), 713-737, 2020 | 29* | 2020 |
| Hardness of parameter estimation in graphical models G Bresler, D Gamarnik, D Shah Advances in Neural Information Processing Systems, 1062-1070, 2014 | 27 | 2014 |
| Optimal average-case reductions to sparse pca: From weak assumptions to strong hardness M Brennan, G Bresler Conference on Learning Theory, 469-470, 2019 | 25 | 2019 |
| Collaborative filtering with low regret G Bresler, D Shah, LF Voloch Proceedings of the 2016 ACM SIGMETRICS International Conference on …, 2016 | 23* | 2016 |
| Reducibility and statistical-computational gaps from secret leakage M Brennan, G Bresler Conference on Learning Theory, 648-847, 2020 | 15 | 2020 |
| Learning restricted Boltzmann machines via influence maximization G Bresler, F Koehler, A Moitra Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 15 | 2019 |
| Optimal single sample tests for structured versus unstructured network data G Bresler, D Nagaraj Conference On Learning Theory, 1657-1690, 2018 | 15 | 2018 |
| Universality of computational lower bounds for submatrix detection M Brennan, G Bresler, W Huleihel Conference on Learning Theory, 417-468, 2019 | 12 | 2019 |
David TseThomas Kailath and Guanghan Xu Professor, Stanford UniversityVerified email at stanford.edu
