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ORCIDDiana Cai
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2020 – today
- 2024
[c7]Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. ICML 2024- [i5]Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. CoRR abs/2402.14758 (2024) - 2023
- [b1]Diana Cai:
Probabilistic Inference When the Model Is Wrong. Princeton University, USA, 2023 - [i4]Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara E. Engelhardt:
Kernel Density Bayesian Inverse Reinforcement Learning. CoRR abs/2303.06827 (2023) - 2022
- [c6]Diana Cai, Ryan P. Adams:
Multi-fidelity Monte Carlo: a pseudo-marginal approach. NeurIPS 2022 - [i3]Diana Cai, Ryan P. Adams:
Multi-fidelity Monte Carlo: a pseudo-marginal approach. CoRR abs/2210.01534 (2022) - 2021
- [c5]Diana Cai, Trevor Campbell, Tamara Broderick:
Finite mixture models do not reliably learn the number of components. ICML 2021: 1158-1169 - [c4]David M. Zoltowski, Diana Cai, Ryan P. Adams:
Slice Sampling Reparameterization Gradients. NeurIPS 2021: 23532-23544 - [c3]Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams:
Active multi-fidelity Bayesian online changepoint detection. UAI 2021: 1916-1926 - [i2]Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams:
Active multi-fidelity Bayesian online changepoint detection. CoRR abs/2103.14224 (2021) - 2020
- [i1]Diana Cai, Rishit Sheth, Lester Mackey, Nicoló Fusi:
Weighted Meta-Learning. CoRR abs/2003.09465 (2020)
2010 – 2019
- 2018
- [c2]Diana Cai, Michael Mitzenmacher, Ryan P. Adams:
A Bayesian Nonparametric View on Count-Min Sketch. NeurIPS 2018: 8782-8791 - 2016
- [c1]Diana Cai, Trevor Campbell, Tamara Broderick:
Edge-exchangeable graphs and sparsity. NIPS 2016: 4242-4250
Coauthor Index
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