Guy Bresler

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David TseThomas Kailath and Guanghan Xu Professor, Stanford UniversityEmail verificata su stanford.edu
Dheeraj NagarajResearch Scientist, GoogleEmail verificata su google.com
Devavrat ShahMassachusetts Institute of TechnologyEmail verificata su mit.edu
Allan SlyDepartment of Mathmatics, Princeton University Department of Statistics, University of CaliforniaEmail verificata su princeton.edu
Dustin CartwrightDepartment of Mathematics, University of TennesseeEmail verificata su utk.edu
Enric Boix-AdseràPhD Student in EECS, MITEmail verificata su mit.edu
Mina KarzandMITEmail verificata su mit.edu
David GamarnikProfessor of Operations Research, MITEmail verificata su mit.edu
Elchanan MosselProfessor of Mathematics, MITEmail verificata su mit.edu
Abhay Parekh(Ex) Adjunct Professor, Electrical Engineering and Computer Science, U C BerkeleyEmail verificata su eecs.berkeley.edu
Brice HuangGraduate student in EECS, MITEmail verificata su mit.edu
Shankar BhamidiProfessor, University of North Carolina, Chapel HillEmail verificata su email.unc.edu
Frederic KoehlerUniversity of ChicagoEmail verificata su uchicago.edu
Kiril BangachevPhD student in Computer ScienceEmail verificata su mit.edu
George H. ChenAssociate Professor, Carnegie Mellon UniversityEmail verificata su cmu.edu
Samuel B. HopkinsMassachusetts Institute of TechnologyEmail verificata su mit.edu
Jerry LiMicrosoft ResearchEmail verificata su microsoft.com
Tselil SchrammStanford UniversityEmail verificata su stanford.edu
Praneeth NetrapalliGoogle ResearchEmail verificata su google.com
Prateek JainGoogle Research IndiaEmail verificata su google.com

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Guy Bresler

MIT

Email verificata su mit.edu - Home page

Information theory statistics probability


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	271	2008
Efficiently learning Ising models on arbitrary graphs G Bresler Proceedings of the forty-seventh annual ACM symposium on Theory of computing …, 2015	226	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	224*	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	210	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	181*	2013
Information Theory of DNA Shotgun Sequencing A Motahari, G Bresler, D Tse IEEE Transactions on Information Theory, 1-1, 2013	152	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	124	2018
Reducibility and statistical-computational gaps from secret leakage M Brennan, G Bresler Conference on Learning Theory, 648-847, 2020	108*	2020
Optimal assembly for high throughput shotgun sequencing G Bresler, M Bresler, D Tse BMC bioinformatics 14 (5), 1-13, 2013	100	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	91*	2021
A Latent Source Model for Online Collaborative Filtering G Bresler, GH Chen, D Shah Advances in Neural Information Processing Systems, 3347-3355, 2014	88	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	67	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	66	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	64	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	59	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	51*	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	50	2022
Structure learning of antiferromagnetic Ising models G Bresler, D Gamarnik, D Shah Advances in Neural Information Processing Systems, 2852-2860, 2014	47	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	45	2019

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