1 citations · 1 across the 6 of their papers we have counts for
7 papers
Posterior sampling by source-space MCMC via prior-based few-step transport maps
Hoang Phuc Hau Luu, Marcelo Hartmann, Zhongjian Wang
Bayesian inference increasingly uses informative but implicit priors represented only by samples, such as historical ensembles, simulator outputs, and pretrained generative models.…
DC-LA: Difference-of-Convex Langevin Algorithm
Hoang Phuc Hau Luu, Zhongjian Wang
We study a sampling problem whose target distribution is where the data fidelity term is Lipschitz smooth while the regularizer term is a non-…
GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Arash Jamshidi, Lauri Seppäläinen, Katsiaryna Haitsiukevich +3
Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading…
Geodesic Slice Sampler for Multimodal Distributions with Strong Curvature
Bernardo Williams, Hanlin Yu, Hoang Phuc Hau Luu +2
Traditional Markov Chain Monte Carlo sampling methods often struggle with sharp curvatures, intricate geometries, and multimodal distributions. Slice sampling can resolve local exp…
Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams +2
Optimization in the Bures-Wasserstein space has been gaining popularity in the machine learning community since it draws connections between variational inference and Wasserstein g…
Non-geodesically-convex optimization in the Wasserstein space
Hoang Phuc Hau Luu, Hanlin Yu, Bernardo Williams +4
We study a class of optimization problems in the Wasserstein space (the space of probability measures) where the objective function is nonconvex along generalized geodesics. Specif…
Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction
Anri Patron, Ayush Prasad, Hoang Phuc Hau Luu +1
A fundamental problem in supervised learning is to find a good set of features or distance measures. If the new set of features is of lower dimensionality and can be obtained by a…