9 citations · 16 across the 8 of their papers we have counts for
6 papers · 1 filter
Posterior concentration and fast convergence rates for generalized Bayesian learning
Lam Si Tung Ho, Binh T. Nguyen, Vu Dinh +1
In this paper, we study the learning rate of generalized Bayes estimators in a general setting where the hypothesis class can be uncountable and have an irregular shape, the loss f…
When can we reconstruct the ancestral state? A unified theory
Lam Si Tung Ho, Vu Dinh
Ancestral state reconstruction is one of the most important tasks in evolutionary biology. Conditions under which we can reliably reconstruct the ancestral state have been studied…
Searching for Minimal Optimal Neural Networks
Lam Si Tung Ho, Vu Dinh
Large neural network models have high predictive power but may suffer from overfitting if the training set is not large enough. Therefore, it is desirable to select an appropriate…
OASIS: An Active Framework for Set Inversion
Binh T. Nguyen, Duy M. Nguyen, Lam Si Tung Ho +1
In this work, we introduce a novel method for solving the set inversion problem by formulating it as a binary classification problem. Aiming to develop a fast algorithm that can wo…
Convergence of maximum likelihood supertree reconstruction
Lam Si Tung Ho, Vu Dinh
Supertree methods are tree reconstruction techniques that combine several smaller gene trees (possibly on different sets of species) to build a larger species tree. The question of…
Convergence of Griddy Gibbs Sampling and other perturbed Markov chains
Vu Dinh, Ann E. Rundell, Gregery T. Buzzard
The Griddy Gibbs sampling was proposed by Ritter and Tanner (1992) as a computationally efficient approximation of the well-known Gibbs sampling method. The algorithm is simple and…