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Approximate inference

Approximate inference

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:


Multi-fidelity Monte Carlo: a pseudo-marginal approach

Advances in Neural Information Processing Systems (NeurIPS), to appear, 2022

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Slice sampling reparameterization gradients

Advances in Neural Information Processing Systems (NeurIPS), 2021
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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

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