Preprints & Working Papers

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

arXiv:1809.09505 [stat.TH], 2018.

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

arXiv:1806.10234 [stat.ML], 2018.

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

arXiv:1810.04249 [stat.ML], 2018.

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Publications

Random feature Stein discrepancies

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

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Truncated random measures

Bernoulli, 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 (NIPS), 2017.

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Quantifying the accuracy of approximate diffusions and Markov chains

In Proc. of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

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Coresets for scalable Bayesian logistic regression

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

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

In Proc. of the 32nd International Conference on Machine Learning (ICML), 2016.

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Risk and regret of hierarchical Bayesian learners

In Proc. of the 32nd International Conference on Machine Learning (ICML), 2016.

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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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Thesis

Scaling Bayesian inference: theoretical foundations and practical methods

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

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Miscellanea

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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Recent & Upcoming Talks

Generic finite approximations for practical Bayesian nonparametrics
Dec 8, 2017
Truncated random measures
Jun 26, 2017

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