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09/11/2023
Automatic regenerative simulation via non-reversible simulated tempering
Simulated Tempering (ST) is an MCMC algorithm for complex target distrib...
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08/29/2023
Mixed Variational Flows for Discrete Variables
Variational flows allow practitioners to learn complex continuous distri...
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08/18/2023
Pigeons.jl: Distributed Sampling From Intractable Distributions
We introduce a software package, Pigeons.jl, that provides a way to leve...
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07/12/2023
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
In this paper, we investigate the impact of numerical instability on the...
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04/21/2023
Machine Learning and the Future of Bayesian Computation
Bayesian models are a powerful tool for studying complex data, allowing ...
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06/17/2022
Conditional Permutation Invariant Flows
We present a novel, conditional generative probabilistic model of set-va...
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05/31/2022
Parallel Tempering With a Variational Reference
Sampling from complex target distributions is a challenging task fundame...
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05/16/2022
Ergodic variational flows
This work presents a new class of variational family – ergodic variation...
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03/18/2022
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Bayesian coresets approximate a posterior distribution by building a sma...
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03/11/2022
Bayesian inference via sparse Hamiltonian flows
A Bayesian coreset is a small, weighted subset of data that replaces the...
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05/28/2021
Pseudo-marginal Inference for CTMCs on Infinite Spaces via Monotonic Likelihood Approximations
Bayesian inference for Continuous-Time Markov Chains (CTMCs) on countabl...
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04/13/2021
The computational asymptotics of Gaussian variational inference
Variational inference is a popular alternative to Markov chain Monte Car...
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02/15/2021
Parallel Tempering on Optimized Paths
Parallel tempering (PT) is a class of Markov chain Monte Carlo algorithm...
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11/27/2020
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture
We present a Physics-Informed Neural Network (PINN) to simulate the ther...
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07/08/2020
Finite mixture models are typically inconsistent for the number of components
Scientists and engineers are often interested in learning the number of ...
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06/24/2020
Slice Sampling for General Completely Random Measures
Completely random measures provide a principled approach to creating fle...
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05/17/2020
Truncated Self-Product Measures in Edge-Exchangeable Networks
Edge-exchangeable probabilistic network models generate edges as an i.i....
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10/09/2019
Practical Posterior Error Bounds from Variational Objectives
Variational inference has become an increasingly attractive, computation...
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06/22/2019
Local Exchangeability
Exchangeability---in which the distribution of an infinite sequence is i...
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06/07/2019
Sparse Variational Inference: Bayesian Coresets from Scratch
The proliferation of automated inference algorithms in Bayesian statisti...
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06/04/2019
Universal Boosting Variational Inference
Boosting variational inference (BVI) approximates an intractable probabi...
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11/28/2018
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Until recently, transcriptomics was limited to bulk RNA sequencing, obsc...
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10/09/2018
Data-dependent compression of random features for large-scale kernel approximation
Kernel methods offer the flexibility to learn complex relationships in m...
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09/25/2018
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Bayesian inference typically requires the computation of an approximatio...
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06/26/2018
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
Gaussian processes (GPs) offer a flexible class of priors for nonparamet...
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02/05/2018
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Coherent uncertainty quantification is a key strength of Bayesian method...
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10/13/2017
Automated Scalable Bayesian Inference via Hilbert Coresets
The automation of posterior inference in Bayesian data analysis has enab...
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07/26/2017
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models
Bayesian nonparametrics are a class of probabilistic models in which the...
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05/20/2016
Coresets for Scalable Bayesian Logistic Regression
The use of Bayesian methods in large-scale data settings is attractive b...
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03/15/2016
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Point cloud alignment is a common problem in computer vision and robotic...
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10/30/2015
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This paper presents a methodology for creating streaming, distributed in...
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05/28/2013