Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
NeurIPS 2024 (to appear)
Control Variate-based Stochastic Sampling from the Probability Simplex
arXiv preprint
Probabilistic Inversion Modeling of Gas Emissions: A Gradient-Based MCMC Estimation of Gaussian Plume Parameters
arXiv preprint
Metropolis-Hastings with Scalable Subsampling
arXiv preprint
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
International Conference on Machine Learning (ICML)
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
International Conference on Machine Learning (ICML)
Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers
arXiv preprint
Diffusion Generative Modelling for Divide-and-Conquer MCMC
arXiv preprint
Robust Bayesian Nonparametric Variable Selection for Linear Regression
Stat
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
International Conference on Artificial Intelligence and Statistics (AISTATS)
Spatial Latent Gaussian Modelling with Change of Support
arXiv preprint
Latent space modelling of hypergraph data
Journal of the American Statistical Association
SwISS: A scalable Markov chain Monte Carlo divide-and-conquer strategy
Stat
Learning Rate Free Sampling in Constrained Domains
Neural Information Processing Systems (NeurIPS)
A changepoint approach to modelling non-stationary soil moisture dynamics
arXiv preprint
Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data
Journal of Glaciology
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
International Conference on Machine Learning (ICML)
Stochastic Gradient MCMC for Nonlinear State Space Models
Bayesian Analysis
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
International Conference on Artificial Intelligence and Statistics (AISTATS)
Transport Elliptical Slice Sampling
International Conference on Artificial Intelligence and Statistics (AISTATS)