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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Probabilistic Inference When the Model is Wrong

PhD Dissertation, Princeton University, 2023

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Kernel density Bayesian inverse reinforcement learning

Submitted (preliminary version appeared in AABI), 2023

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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Efficient Bayesian inverse reinforcement learning via conditional kernel density estimation

Symposium on Advances in Approximate Bayesian Inference, 2022

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Slice sampling reparameterization gradients

Advances in Neural Information Processing Systems (NeurIPS), 2021
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Finite mixture models do not reliably learn the number of components

Proceedings of the 38th International Conference on Machine Learning (ICML), 2021
Oral presentation (short)

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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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Power posteriors do not reliably learn the number of components in a finite mixture

NeurIPS Workshop: I Can’t Believe It’s Not Better, 2020
Best Paper Award (Didactic Track) & spotlight presentation

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Weighted meta-learning

ICML Workshop on Automated Machine Learning, 2020

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Weighted meta-learning

arXiv e-print 2003.09465, 2020

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Finite mixture models are typically inconsistent for the number of components

NeurIPS Workshop on Machine Learning With Guarantees, 2019

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Exchangeable trait allocations

Electronic Journal of Statistics, 2018

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A Bayesian nonparametric view on count-min sketch

Advances in Neural Information Processing Systems (NeurIPS), 2018

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Finite mixture models are typically inconsistent for the number of components

NeurIPS Workshop on Advances in Approximate Bayesian Inference (AABI), 2017

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Paintboxes and probability functions for edge-exchangeable graphs

NeurIPS Workshop on Adaptive and Scalable Nonparametric Methods in Machine Learning, 2016
Oral presentation

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Edge-exchangeable graphs and sparsity

Advances in Neural Information Processing Systems (NeurIPS), 2016
ISBA@NeurIPS Award at NeurIPS Workshop on Bayesian Nonparametrics

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Priors on exchangeable directed graphs

Electronic Journal of Statistics, 2016

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Edge-exchangeable graphs, sparsity, and power laws

NeurIPS Workshop on Bayesian Nonparametrics, 2015
ISBA@NeurIPS Award & oral presentation

Edge-exchangeable graphs and sparsity

NeurIPS Workshop on Networks in the Social and Information Sciences, 2015
Spotlight presentation

Completely random measures for modeling power laws in graphs

NeurIPS 2015 Workshop on Networks in the Social and Information Sciences, 2015
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An iterative step-function estimator for graphons

arXiv e-print 1412.2129, 2014

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Scalable methods for Bayesian online changepoint detection

Harvard College Senior Thesis in Computer Science and Statistics, 2014