Tuning-Free Sampling via Optimization on the Space of Probability Measures
arXiv preprint
Probabilistic Inversion Modeling of Gas Emissions: A Gradient-Based MCMC Estimation of Gaussian Plume Parameters
Annals of Applied Statistics (to appear)
Inferring Soil Drydown Behaviour with Adaptive Bayesian Online Changepoint Analysis
arXiv preprint
Modelling Populations of Interaction Networks via Distance Metrics
Journal of Machine Learning Research
A changepoint approach to modelling non-stationary soil moisture dynamics
Journal of the Royal Statistical Society Series C: Applied Statistics
Deep Learning Surrogates for Real-Time Gas Emission Inversion
arXiv preprint
Stochastic Gradient MCMC for Nonlinear State Space Models
Bayesian Analysis
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
NeurIPS 2024
Control Variate-based Stochastic Sampling from the Probability Simplex
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)