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Found 35 papers, 17 papers with code
Tuning-free coreset Markov chain Monte Carlo
A Bayesian coreset is a small, weighted subset of a data set that replaces the full data during inference to reduce computational cost.
AutoStep: Locally adaptive involutive MCMC
Many common Markov chain Monte Carlo (MCMC) kernels can be formulated using a deterministic involutive proposal with a step size parameter.
General bounds on the quality of Bayesian coresets
The lower bounds require only mild model assumptions typical of Bayesian asymptotic analyses, while the upper bounds require the log-likelihood functions to satisfy a generalized subexponentiality criterion that is weaker than conditions used in earlier work.
MCMC-driven learning
This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo.
Coreset Markov Chain Monte Carlo
A Bayesian coreset is a small, weighted subset of data that replaces the full dataset during inference in order to reduce computational cost.
Mixed Variational Flows for Discrete Variables
First, we develop a measure-preserving and discrete (MAD) invertible map that leaves the discrete target invariant, and then create a mixed variational flow (MAD Mix) based on that map.
Machine Learning and the Future of Bayesian Computation
Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information.
Conditional Permutation Invariant Flows
We present a novel, conditional generative probabilistic model of set-valued data with a tractable log density.
MixFlows: principled variational inference via mixed flows
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.
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points.
Bayesian inference via sparse Hamiltonian flows
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.
The computational asymptotics of Gaussian variational inference and the Laplace approximation
Gaussian variational inference and the Laplace approximation are popular alternatives to Markov chain Monte Carlo that formulate Bayesian posterior inference as an optimization problem, enabling the use of simple and scalable stochastic optimization algorithms.
Parallel Tempering on Optimized Paths
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, and then interchanges states along the path to improve mixing in the target.
Computation 65C05
Bayesian Pseudocoresets
Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern large-scale data.
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 thermochemical evolution of a composite material on a tool undergoing cure in an autoclave.
Power posteriors do not reliably learn the number of components in a finite mixture
Increasingly, though, data science papers suggest potential alternatives beyond vanilla FMMs, such as power posteriors, coarsening, and related methods.
Finite mixture models do not reliably learn the number of components
In this paper, we add rigor to data-analysis folk wisdom by proving that under even the slightest model misspecification, the FMM component-count posterior diverges: the posterior probability of any particular finite number of components converges to 0 in the limit of infinite data.
Slice Sampling for General Completely Random Measures
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 influence the data grows with the size of the data set.
Validated Variational Inference via Practical Posterior Error Bounds
Finally, we demonstrate the utility of our proposed workflow and error bounds on a robust regression problem and on a real-data example with a widely used multilevel hierarchical model.
Sparse Variational Inference: Bayesian Coresets from Scratch
But the automation of past coreset methods is limited because they depend on the availability of a reasonable coarse posterior approximation, which is difficult to specify in practice.
Universal Boosting Variational Inference
We show that for any target density and any mixture component family, the output of UBVI converges to the best possible approximation in the mixture family, even when the mixture family is misspecified.
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Specifically, we extend the framework of the classical Dirichlet diffusion tree to simultaneously infer branch topology and latent cell states along continuous trajectories over the full tree.
Data-dependent compression of random features for large-scale kernel approximation
Random feature maps (RFMs) and the Nystrom method both consider low-rank approximations to the kernel matrix as a potential solution.
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Bayesian inference typically requires the computation of an approximation to the posterior distribution.
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
We develop an approach to scalable approximate GP regression with finite-data guarantees on the accuracy of pointwise posterior mean and variance estimates.
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Coherent uncertainty quantification is a key strength of Bayesian methods.
Automated Scalable Bayesian Inference via Hilbert Coresets
We begin with an intuitive reformulation of Bayesian coreset construction as sparse vector sum approximation, and demonstrate that its automation and performance-based shortcomings arise from the use of the supremum norm.
Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models
Bayesian nonparametrics are a class of probabilistic models in which the model size is inferred from data.
Edge-exchangeable graphs and sparsity (NIPS 2016)
Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices.
Small-Variance Nonparametric Clustering on the Hypersphere
Based on the small-variance limit of Bayesian nonparametric von-Mises-Fisher (vMF) mixture distributions, we propose two new flexible and efficient k-means-like clustering algorithms for directional data such as surface normals.
Coresets for Scalable Bayesian Logistic Regression
We demonstrate the efficacy of our approach on a number of synthetic and real-world datasets, and find that, in practice, the size of the coreset is independent of the original dataset size.
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction.
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
This paper presents a methodology for creating streaming, distributed inference algorithms for Bayesian nonparametric (BNP) models.
Approximate Decentralized Bayesian Inference
The method first employs variational inference on each individual learning agent to generate a local approximate posterior, the agents transmit their local posteriors to other agents in the network, and finally each agent combines its set of received local posteriors.
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters.
