2024
- Scalable Monte Carlo for Bayesian Learning
- Tuning-free maximum likelihood training of latent variable models via coin betting
- Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
- Robust Bayesian nonparametric variable selection for linear regression
- Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
2023
- Latent space modeling of hypergraph data
- Learning Rate Free Sampling in Constrained Domains
- Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
- Efficient and generalizable tuning strategies for stochastic gradient MCMC
- Multivariate sensitivity analysis for a large-scale climate impact and adaptation model
- Preferential Subsampling for Stochastic Gradient Langevin Dynamics
- Transport Elliptical Slice Sampling
- Sequential estimation of temporally evolving latent space network models
- Stochastic gradient MCMC for nonlinear state space model