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Diana Cai
Diana Cai
Center for Computational Mathematics, Flatiron Institute
Verified email at flatironinstitute.org - Homepage
Title
Cited by
Cited by
Year
Edge-exchangeable graphs and sparsity
D Cai, T Campbell, T Broderick
Advances in Neural Information Processing Systems 29, 4242-4250, 2016
982016
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
32*2021
Exchangeable trait allocations
T Campbell, D Cai, T Broderick
Electronic Journal of Statistics 12 (2), 2290-2322, 2018
272018
Weighted Meta-Learning
D Cai, R Sheth, L Mackey, N Fusi
ICML 2020 Workshop on Automated Machine Learning, 2020
182020
An iterative step-function estimator for graphons
D Cai, N Ackerman, C Freer
arXiv preprint arXiv:1412.2129, 2014
182014
A Bayesian Nonparametric View on Count-Min Sketch
D Cai, M Mitzenmacher, RP Adams
Advances in Neural Information Processing Systems 31, 8781-8790, 2018
172018
Edge-exchangeable graphs, sparsity, and power laws
T Broderick, D Cai
NIPS 2015 Workshop on Bayesian Nonparametrics: The Next Generation 5, 2015
13*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
82021
Priors on exchangeable directed graphs
D Cai, N Ackerman, C Freer
Electronic Journal of Statistics 10 (2), 3490-3515, 2016
82016
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
82015
Slice sampling reparameterization gradients
DM Zoltowski, D Cai, RP Adams
Advances in Neural Information Processing Systems 34, 23532-23544, 2021
52021
Kernel density Bayesian inverse reinforcement learning
A Mandyam, D Li, D Cai, A Jones, BE Engelhardt
arXiv preprint arXiv:2303.06827, 2023
4*2023
Multi-fidelity Monte Carlo: a pseudo-marginal approach
D Cai, RP Adams
Advances in Neural Information Processing Systems 35, 2022
32022
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
22020
Optimizing the design of spatial genomic studies
A Jones, D Cai, D Li, BE Engelhardt
Nature Communications 15, 4987, 2024
1*2024
Batch and match: black-box variational inference with a score-based divergence
D Cai, C Modi, L Pillaud-Vivien, CC Margossian, RM Gower, DM Blei, ...
Proceedings of the 41st International Conference on Machine Learning, 2024
12024
Probabilistic Prediction of Material Stability: Integrating Convex Hulls into Active Learning
A Novick, D Cai, Q Nguyen, R Garnett, RP Adams, E Toberer
arXiv preprint arXiv:2402.15582, 2024
2024
Probabilistic Inference When the Model Is Wrong
D Cai
Princeton University, 2023
2023
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Articles 1–18