Guy Bresler

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David TseThomas Kailath and Guanghan Xu Professor, Stanford UniversityAdresse e-mail validée de stanford.edu
Dheeraj NagarajResearch Scientist, GoogleAdresse e-mail validée de google.com
Devavrat ShahMassachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Allan SlyDepartment of Mathmatics, Princeton University Department of Statistics, University of CaliforniaAdresse e-mail validée de princeton.edu
Enric Boix-AdseràPhD Student in EECS, MITAdresse e-mail validée de mit.edu
Mina KarzandMITAdresse e-mail validée de mit.edu
David GamarnikProfessor of Operations Research, MITAdresse e-mail validée de mit.edu
Elchanan MosselProfessor of Mathematics, MITAdresse e-mail validée de mit.edu
Brice HuangGraduate student in EECS, MITAdresse e-mail validée de mit.edu
Shankar BhamidiProfessor, University of North Carolina, Chapel HillAdresse e-mail validée de email.unc.edu
Frederic KoehlerUniversity of ChicagoAdresse e-mail validée de uchicago.edu
George H. ChenAssociate Professor, Carnegie Mellon UniversityAdresse e-mail validée de cmu.edu
Samuel B. HopkinsMassachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Jerry LiUniversity of WashingtonAdresse e-mail validée de cs.washington.edu
Tselil SchrammStanford UniversityAdresse e-mail validée de stanford.edu
Praneeth NetrapalliGoogle ResearchAdresse e-mail validée de google.com
Prateek JainGoogle Research IndiaAdresse e-mail validée de google.com
Emmanuel AbbeProfessor EPFL, Senior Research Scientist AppleAdresse e-mail validée de epfl.ch
Ankur MoitraNorbert Wiener Professor of Mathematics, MITAdresse e-mail validée de mit.edu
Chenghao GuoMassachusetts Institute of TechnologyAdresse e-mail validée de mit.edu

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Guy Bresler

MIT

Adresse e-mail validée de mit.edu - Page d'accueil

Information theory statistics probability


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
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	376	2010
The two‐user Gaussian interference channel: a deterministic view G Bresler, D Tse European transactions on telecommunications 19 (4), 333-354, 2008	272	2008
Efficiently learning Ising models on arbitrary graphs G Bresler Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015	232	2015
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	226*	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	213	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	184*	2013
Information Theory of DNA Shotgun Sequencing A Motahari, G Bresler, D Tse IEEE Transactions on Information Theory, 1-1, 2013	153	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	125	2018
Reducibility and statistical-computational gaps from secret leakage M Brennan, G Bresler Conference on Learning Theory, 648-847, 2020	110*	2020
Optimal assembly for high throughput shotgun sequencing G Bresler, M Bresler, D Tse BMC bioinformatics 14 (5), 1-13, 2013	102	2013
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	97*	2021
A Latent Source Model for Online Collaborative Filtering G Bresler, GH Chen, D Shah Advances in Neural Information Processing Systems, 3347-3355, 2014	90	2014
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	75	2021
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	74	2020
Optimal average-case reductions to sparse pca: From weak assumptions to strong hardness M Brennan, G Bresler Conference on Learning Theory, 469-470, 2019	67	2019
Learning a tree-structured Ising model in order to make predictions G Bresler, M Karzand The Annals of Statistics 48 (2), 713-737, 2020	60	2020
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	55	2022
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	52*	2009
The Average-Case Complexity of Counting Cliques in Erdős-Rényi Hypergraphs E Boix-Adserà, M Brennan, G Bresler 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS …, 2019	50	2019
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	48	2019

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