Artigos com autorizações de acesso público - Moses CharikarSaiba mais
1 artigo não disponível publicamente
Unconditional lower bounds for adaptive massively parallel computation
M Charikar, W Ma, LY Tan
Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and …, 2020
Autorizações: US National Science Foundation
39 artigos disponíveis publicamente
Learning from untrusted data
M Charikar, J Steinhardt, G Valiant
Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, 2017
Autorizações: US National Science Foundation
Targeted exploration and analysis of large cross-platform human transcriptomic compendia
Q Zhu, AK Wong, A Krishnan, MR Aure, A Tadych, R Zhang, DC Corney, ...
Nature methods 12 (3), 211-214, 2015
Autorizações: US National Institutes of Health
Approximate hierarchical clustering via sparsest cut and spreading metrics
M Charikar, V Chatziafratis
Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017
Autorizações: US National Science Foundation
Hashing-based-estimators for kernel density in high dimensions
M Charikar, P Siminelakis
2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS …, 2017
Autorizações: US National Science Foundation
Multireference alignment using semidefinite programming
AS Bandeira, M Charikar, A Singer, A Zhu
Proceedings of the 5th conference on Innovations in theoretical computer …, 2014
Autorizações: US National Institutes of Health
Hierarchical clustering better than average-linkage
M Charikar, V Chatziafratis, R Niazadeh
Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete …, 2019
Autorizações: US National Science Foundation
Sampling methods for counting temporal motifs
P Liu, AR Benson, M Charikar
Proceedings of the twelfth ACM international conference on web search and …, 2019
Autorizações: US National Science Foundation
Hierarchical clustering with structural constraints
V Chatziafratis, R Niazadeh, M Charikar
International conference on machine learning, 774-783, 2018
Autorizações: US National Science Foundation, US Department of Defense
Fully dynamic almost-maximal matching: Breaking the polynomial worst-case time barrier
M Charikar, S Solomon
45th International Colloquium on Automata, Languages, and Programming (ICALP …, 2018
Autorizações: US National Science Foundation
Rehashing kernel evaluation in high dimensions
P Siminelakis, K Rong, P Bailis, M Charikar, P Levis
International Conference on Machine Learning, 5789-5798, 2019
Autorizações: US National Science Foundation
Avoiding imposters and delinquents: Adversarial crowdsourcing and peer prediction
J Steinhardt, G Valiant, M Charikar
Advances in Neural Information Processing Systems 29, 2016
Autorizações: US National Science Foundation
Efficient density evaluation for smooth kernels
A Backurs, M Charikar, P Indyk, P Siminelakis
2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018
Autorizações: US National Science Foundation
Local guarantees in graph cuts and clustering
M Charikar, N Gupta, R Schwartz
International Conference on Integer Programming and Combinatorial …, 2017
Autorizações: US National Science Foundation
Kernel density estimation through density constrained near neighbor search
M Charikar, M Kapralov, N Nouri, P Siminelakis
2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS …, 2020
Autorizações: US Department of Defense, European Commission
Efficient profile maximum likelihood for universal symmetric property estimation
M Charikar, K Shiragur, A Sidford
Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019
Autorizações: US National Science Foundation
Multiway online correlated selection
G Blanc, M Charikar
2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022
Autorizações: US National Science Foundation
Metric distortion bounds for randomized social choice
M Charikar, P Ramakrishnan
Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2022
Autorizações: US National Science Foundation
Local density estimation in high dimensions
X Wu, M Charikar, V Natchu
International Conference on Machine Learning, 5296-5305, 2018
Autorizações: US National Science Foundation
Near-Optimal Explainable k-Means for All Dimensions
M Charikar, L Hu
Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2022
Autorizações: US National Science Foundation
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