Publications

EigenVI: score-based variational inference with orthogonal function expansions

Advances in Neural Information Processing Systems (NeurIPS), 2024
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Batch, match, and patch: low-rank approximations for score-based variational inference

arXiv e-print 2410.22292, 2024

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Kernel density Bayesian inverse reinforcement learning

Transactions on Machine Learning Research, 2024

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

Materials Horizons, 2024

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

Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
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Optimizing the design of spatial genomics studies

Nature Communications, 2024

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Probabilistic Inference When the Model is Wrong

PhD Dissertation, Princeton University, 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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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
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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
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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
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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