Diana Cai
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Recent & Upcoming Talks
2024
Batch and match: score-based approaches to black-box variational inference
Nov 12, 2024
International Seminar on Monte Carlo Methods
Batch and match: black-box variational inference using a score-based divergence
Jul 2, 2024
ISBA World Meeting
Batch and match: black-box variational inference using a score-based divergence
May 24, 2024
New England Statistics Symposium
2023
Probabilistic inference when the model is wrong
Jul 23, 2023
Symposium on Advances in Approximate Bayesian Inference
Multi-fidelity scientific discovery and design
Apr 16, 2023
Virtual Seminar Series on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
Probabilistic inference under misspecification
Jan 23, 2023
Harvard Statistics Department
2022
Multi-fidelity Monte Carlo: a pseudo-marginal approach
May 24, 2022
35th New England Statistics Symposium, University of Connecticut
Finite mixture models do not reliably learn the number of components
Feb 2, 2022
ASA Conference on Statistical Practice
2021
Finite mixture models do not reliably learn the number of components
Nov 3, 2021
BIRS-Oaxaca Foundations of Objective Bayesian Methodology Workshop
Finite mixture models do not reliably learn the number of components
Oct 2, 2021
34th New England Statistics Symposium, University of Rhode Island
Finite mixture models do not reliably learn the number of components
Sep 15, 2021
Aalto University Seminar on Advances in Probabilistic Machine Learning
Finite mixture models do not reliably learn the number of components
Jul 2, 2021
ISBA World Meeting Online
2020
Finite mixture models do not reliably learn the number of components
Nov 16, 2020
Bayesian Young Statisticians Meeting Online
Probabilistic inference under model misspecification
Oct 26, 2020
University of Maryland Center for Machine Learning
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