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Approximate inference & decision making

Approximate inference & decision making

In many domains in science, medicine, and engineering, computational methods for simulation, uncertainty quantification, and experimental design are becoming increasingly important. However, a key challenge in these domains is dealing with computationally expensive methods. In some problems, massive and high-dimensional data require algorithms that can scale to those settings. In other problems, accurate models for complex systems are expensive to evaluate, such as an expensive physical simulation, or observations may be expensive to gather.

I am developing computational methods with the goal of accelerating and improving the performance of approximate Bayesian inference methods and methods for decision making under uncertainty. Some current research directions include:

Publications

Batch and match: black-box variational inference using a score-based divergence

Proceedings of the 41st International Conference on Machine Learning (ICML), to appear, 2024

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Probabilistic prediction of material stability: integrating convex hulls into Bayesian active learning

arXiv preprint, 2024

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Optimizing the design of spatial genomics studies

In revision, 2023

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Multi-fidelity Monte Carlo: a pseudo-marginal approach

Advances in Neural Information Processing Systems (NeurIPS), 2022

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Multi-fidelity Bayesian experimental design using power posteriors

NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022

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Slice sampling reparameterization gradients

Advances in Neural Information Processing Systems (NeurIPS), 2021
Spotlight presentation

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Active multi-fidelity Bayesian online changepoint detection

Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021

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Slice sampling reparameterization gradients

Symposium on Advances in Approximate Bayesian Inference, 2021

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A Bayesian nonparametric view on count-min sketch

Advances in Neural Information Processing Systems (NeurIPS), 2018

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Diana Cai
Center for Computational Mathematics

I am broadly interested in machine learning and statistics, and in particular, developing robust and reliable methods for modeling and inference.