Publications

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Robust discovery of mutational signatures using power posteriors

bioRxiv 2024.10.23.619958, 2024.

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Tuning-free coreset Markov chain Monte Carlo

arXiv:2410.18973 [stat.CO], 2024.

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A Framework for Improving the Reliability of Black-box Variational Inference

Journal of Machine Learning Research 25(219): 1−71, 2024.

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Reproducible Parameter Inference Using Bagged Posteriors

Electronic Journal of Statistics 18(1): 1549–1585, 2024.

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Independent finite approximations for Bayesian nonparametric inference

Bayesian Analysis, 2024.

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Structurally Aware Robust Model Selection for Mixtures

arXiv:2403.00687 [stat.ME], 2024.

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A Targeted Accuracy Diagnostic for Variational Approximations

In Proc. of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain. PMLR: Volume 108, 2023.

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Reproducible Model Selection Using Bagged Posteriors

Bayesian Analysis 18(1): 79-104, 2023.

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Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics

arXiv:2207.12395 [stat.CO], 2022.

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Calibrated Model Criticism Using Split Predictive Checks

arXiv:2203.15897 [stat.ME], 2022.

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The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures

Cancer Research 81(23), 2021.

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Challenges and Opportunities in High-dimensional Variational Inference

In Proc. of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS), 2021.

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The feasibility of targeted test-trace-isolate for the control of SARS-CoV-2 variants

F1000Research 10(291), 2021.

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Bidirectional contact tracing could dramatically improve COVID-19 control

Nature Communications 12(232), 2021.

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Robust, Accurate Stochastic Optimization for Variational Inference

In Proc. of the 34th Annual Conference on Neural Information Processing Systems (NeurIPS), 2020.

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Validated Variational Inference via Practical Posterior Error Bounds

In Proc. of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Italy. PMLR: Volume 108, 2020.

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Robust Inference and Model Criticism Using Bagged Posteriors

arXiv:1912.07104 [stat.ME], 2019.

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The kernel interaction trick: fast Bayesian discovery of pairwise interactions in high dimensions

In Proc. of the 36th International Conference on Machine Learning (ICML), Long Beach, California. PMLR: Volume 97, 2019.

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LR-GLM: high-dimensional Bayesian inference using low-rank data approximations

In Proc. of the 36th International Conference on Machine Learning (ICML), Long Beach, California. PMLR: Volume 97, 2019.

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Scalable Gaussian process inference with finite-data mean and variance guarantees

In Proc. of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan. PMLR: Volume 89, 2019.

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Data-dependent compression of random features for large-scale kernel approximation

In Proc. of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan. PMLR: Volume 89, 2019.

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Truncated random measures

Bernoulli 25(2): 1256-1288, 2019.

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Sequential Monte Carlo as approximate sampling: bounds, adaptive resampling via ∞-ESS, and an application to Particle Gibbs

Bernoulli 25(1): 584–622, 2019.

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Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data

arXiv:1811.11790 [q-bio.QM], 2018.

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Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach

arXiv:1809.09505 [stat.TH], 2018.

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Random feature Stein discrepancies

In Proc. of the 32nd Annual Conference on Neural Information Processing Systems (NeurIPS), 2018.

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Scaling Bayesian inference: theoretical foundations and practical methods

Ph.D. thesis, Massachusetts Institute of Technology, 2018.

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PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference

In Proc. of the 31st Annual Conference on Neural Information Processing Systems (NeurIPS), 2017.

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Quantifying the accuracy of approximate diffusions and Markov chains

In Proc. of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida, USA. PMLR: Volume 54, 2017.

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Coresets for scalable Bayesian logistic regression

In Proc. of the 30th Annual Conference on Neural Information Processing Systems (NeurIPS), 2016.

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Risk and regret of hierarchical Bayesian learners

In Proc. of the 32nd International Conference on Machine Learning (ICML), Lille, France. PMLR: Volume 37, 2015.

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JUMP-Means: small-variance asymptotics for Markov jump processes

In Proc. of the 32nd International Conference on Machine Learning (ICML), Lille, France. PMLR: Volume 37, 2015.

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Detailed Derivations of Small-variance Asymptotics for some Hierarchical Bayesian Nonparametric Models

arXiv:1501.00052 [stat.ML], 2014.

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Infinite Structured Hidden Semi-Markov Models

arXiv:1407.0044 [stat.ME], 2014.

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Fast Kalman filtering and forward-backward smoothing via a low-rank perturbative approach

Journal of Computational and Graphical Statistics, 23(2): 316–339, 2014.

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A statistical learning theory framework for supervised pattern discovery

In Proc. of SIAM International Conference on Data Mining (SDM), 2014.

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Fast state-space methods for inferring dendritic synaptic connectivity

Journal of Computational Neuroscience, 36(3): 415–443, 2014.

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Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime

Journal of Computational Neuroscience, 32(2): 347–366, 2012.

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