Diana Cai

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Tamara BroderickAssociate Professor of EECS, Massachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Trevor CampbellAssociate Professor, Statistics, UBCAdresse e-mail validée de stat.ubc.ca
Ryan P. AdamsPrinceton UniversityAdresse e-mail validée de princeton.edu
Barbara EngelhardtSenior Investigator, Gladstone Institutes and Professor, Stanford UniversityAdresse e-mail validée de stanford.edu
Cameron E. FreerResearch Scientist, MIT and Project Associate Professor, Keio UniversityAdresse e-mail validée de mit.edu
Nate AckermanHarvard UniversityAdresse e-mail validée de math.harvard.edu
Michael MitzenmacherProfessor of Computer Science, Harvard UniversityAdresse e-mail validée de eecs.harvard.edu
Rishit ShethSenior researcher, Microsoft ResearchAdresse e-mail validée de microsoft.com
Nicolo FusiPartner Researcher, Microsoft ResearchAdresse e-mail validée de microsoft.com
Lester MackeyMicrosoft ResearchAdresse e-mail validée de microsoft.com
David M. ZoltowskiStanford UniversityAdresse e-mail validée de stanford.edu
Gregory GundersenPrinceton UniversityAdresse e-mail validée de princeton.edu
Chuteng ZhouArm Inc. / MITAdresse e-mail validée de arm.com
Didong LiAssistant Professor, Department of Biostatistics, Gillings School of Global Public Health, UNCAdresse e-mail validée de unc.edu
Aishwarya MandyamPhD Student, Stanford UniversityAdresse e-mail validée de stanford.edu

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Diana Cai

Center for Computational Mathematics, Flatiron Institute

Adresse e-mail validée de flatironinstitute.org - Page d'accueil

Machine Learning Bayesian Inference Statistics


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Edge-exchangeable graphs and sparsity D Cai, T Campbell, T Broderick Advances in Neural Information Processing Systems 29, 4242-4250, 2016	93	2016
Finite mixture models do not reliably learn the number of components D Cai, T Campbell, T Broderick Proceedings of the 38th International Conference on Machine Learning 139 …, 2021	30*	2021
Exchangeable trait allocations T Campbell, D Cai, T Broderick Electronic Journal of Statistics 12 (2), 2290-2322, 2018	25	2018
Weighted Meta-Learning D Cai, R Sheth, L Mackey, N Fusi ICML 2020 Workshop on Automated Machine Learning, 2020	17	2020
An iterative step-function estimator for graphons D Cai, N Ackerman, C Freer arXiv preprint arXiv:1412.2129, 2014	17	2014
A Bayesian Nonparametric View on Count-Min Sketch D Cai, M Mitzenmacher, RP Adams Advances in Neural Information Processing Systems 31, 8781-8790, 2018	14	2018
Edge-exchangeable graphs, sparsity, and power laws T Broderick, D Cai NIPS 2015 Workshop on Bayesian Nonparametrics: The Next Generation 5, 2015	13*	2015
Priors on exchangeable directed graphs D Cai, N Ackerman, C Freer Electronic Journal of Statistics 10 (2), 3490-3515, 2016	8	2016
Completely random measures for modeling power laws in sparse graphs D Cai, T Broderick NIPS 2015 Workshop on Networks in the Social and Information Sciences, 2015	8	2015
Active multi-fidelity Bayesian online changepoint detection GW Gundersen, D Cai, C Zhou, BE Engelhardt, RP Adams Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence …, 2021	7	2021
Slice sampling reparameterization gradients DM Zoltowski, D Cai, RP Adams Advances in Neural Information Processing Systems 34, 23532-23544, 2021	4	2021
Multi-fidelity Monte Carlo: a pseudo-marginal approach D Cai, RP Adams Advances in Neural Information Processing Systems 35, 2022	3	2022
Power posteriors do not reliably learn the number of components in a finite mixture D Cai, T Campbell, T Broderick ''I Can't Believe It's Not Better!''NeurIPS 2020 workshop, 2020	2	2020
Kernel density Bayesian inverse reinforcement learning A Mandyam, D Li, D Cai, A Jones, BE Engelhardt arXiv preprint arXiv:2303.06827, 2023	1*	2023
Optimizing the design of spatial genomic studies A Jones, D Cai, D Li, BE Engelhardt bioRxiv, 2023.01. 29.526115, 2023	1*	2023

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