317 citations · 721 across the 36 of their papers we have counts for
38 papers
General bounds on the quality of Bayesian coresets
Trevor Campbell
Bayesian coresets speed up posterior inference in the large-scale data regime by approximating the full-data log-likelihood function with a surrogate log-likelihood based on a smal…
Pigeons.jl: Distributed Sampling From Intractable Distributions
Nikola Surjanovic, Miguel Biron-Lattes, Paul Tiede +3
We introduce a software package, Pigeons.jl, that provides a way to leverage distributed computation to obtain samples from complicated probability distributions, such as multimoda…
Mixed Variational Flows for Discrete Variables
Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell
Variational flows allow practitioners to learn complex continuous distributions, but approximating discrete distributions remains a challenge. Current methodologies typically embed…
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
Zuheng Xu, Trevor Campbell
In this paper, we investigate the impact of numerical instability on the reliability of sampling, density evaluation, and evidence lower bound (ELBO) estimation in variational flow…
Machine Learning and the Future of Bayesian Computation
Steven Winter, Trevor Campbell, Lizhen Lin +2
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they…
Conditional Permutation Invariant Flows
Berend Zwartsenberg, Adam Ścibior, Matthew Niedoba +7
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density. This model is a continuous normalizing flow governed by permutation…
Parallel Tempering With a Variational Reference
Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté +1
Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on…
MixFlows: principled variational inference via mixed flows
Zuheng Xu, Naitong Chen, Trevor Campbell
This work presents mixed variational flows (MixFlows), a new variational family that consists of a mixture of repeated applications of a map to an initial reference distribution. F…
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Cian Naik, Judith Rousseau, Trevor Campbell
Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be r…
Bayesian inference via sparse Hamiltonian flows
Naitong Chen, Zuheng Xu, Trevor Campbell
A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during Bayesian inference, with the goal of reducing computational cost. Although past work ha…
Pseudo-marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations
Miguel Biron-Lattes, Alexandre Bouchard-Côté, Trevor Campbell
Bayesian inference for Continuous-Time Markov Chains (CTMCs) on countably infinite spaces is notoriously difficult because evaluating the likelihood exactly is intractable. One way…
The computational asymptotics of Gaussian variational inference and the Laplace approximation
Zuheng Xu, Trevor Campbell
Gaussian variational inference and the Laplace approximation are popular alternatives to Markov chain Monte Carlo that formulate Bayesian posterior inference as an optimization pro…
Parallel Tempering on Optimized Paths
Saifuddin Syed, Vittorio Romaniello, Trevor Campbell +1
Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithms that constructs a path of distributions annealing between a tractable reference and an intractable target,…
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
Sina Amini Niaki, Ehsan Haghighat, Trevor Campbell +2
We present a Physics-Informed Neural Network (PINN) to simulate the thermochemical evolution of a composite material on a tool undergoing cure in an autoclave. In particular, we so…
Finite mixture models do not reliably learn the number of components
Diana Cai, Trevor Campbell, Tamara Broderick
Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. A common suggestion is to use a finite mixture model (…
Slice Sampling for General Completely Random Measures
Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell
Completely random measures provide a principled approach to creating flexible unsupervised models, where the number of latent features is infinite and the number of features that i…
Truncated Simulation and Inference in Edge-Exchangeable Networks
Xinglong Li, Trevor Campbell
Edge-exchangeable probabilistic network models generate edges as an i.i.d.~sequence from a discrete measure, providing a simple means for statistical inference of latent network pr…
Validated Variational Inference via Practical Posterior Error Bounds
Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell +1
Variational inference has become an increasingly attractive fast alternative to Markov chain Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to th…
Local Exchangeability
Trevor Campbell, Saifuddin Syed, Chiao-Yu Yang +2
Exchangeability -- in which the distribution of an infinite sequence is invariant to reorderings of its elements -- implies the existence of a simple conditional independence struc…
Sparse Variational Inference: Bayesian Coresets from Scratch
Trevor Campbell, Boyan Beronov
The proliferation of automated inference algorithms in Bayesian statistics has provided practitioners newfound access to fast, reproducible data analysis and powerful statistical m…
Universal Boosting Variational Inference
Trevor Campbell, Xinglong Li
Boosting variational inference (BVI) approximates an intractable probability density by iteratively building up a mixture of simple component distributions one at a time, using tec…
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger +4
Until recently, transcriptomics was limited to bulk RNA sequencing, obscuring the underlying expression patterns of individual cells in favor of a global average. Thanks to technol…
Data-dependent compression of random features for large-scale kernel approximation
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins +1
Kernel methods offer the flexibility to learn complex relationships in modern, large data sets while enjoying strong theoretical guarantees on quality. Unfortunately, these methods…
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak +1
Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is…