53 citations · 86 across the 29 of their papers we have counts for
36 papers
rfBLT: Random Feature Bayesian Lasso Takens Model for time series forecasting
Thu Nguyen, Lam Si Tung Ho
Time series prediction is challenging due to our limited understanding of the underlying dynamics. Conventional models such as ARIMA and Holt's linear trend model experience diffic…
Simulation-based Methods for Optimal Sampling Design in Systems Biology
Tuan Minh Ha, Binh Thanh Nguyen, Lam Si Tung Ho
In many areas of systems biology, including virology, pharmacokinetics, and population biology, dynamical systems are commonly used to describe biological processes. These systems…
Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient
Vu C. Dinh, Lam Si Tung Ho, Cuong V. Nguyen
We analyze the error rates of the Hamiltonian Monte Carlo algorithm with leapfrog integrator for Bayesian neural network inference. We show that due to the non-differentiability of…
Nonparametric Inference Framework for Time-dependent Epidemic Models
Son Luu, Edward Susko, Lam Si Tung Ho
Compartmental models, especially the Susceptible-Infected-Removed (SIR) model, have long been used to understand the behaviour of various diseases. Allowing parameters, such as the…
Simple Transferability Estimation for Regression Tasks
Cuong N. Nguyen, Phong Tran, Lam Si Tung Ho +4
We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which receive…
Detection of evolutionary shifts in variance under an Ornsten-Uhlenbeck model
Wensha Zhang, Lam Si Tung Ho, Toby Kenney
Sudden changes in environmental conditions can lead to evolutionary shifts not only in the optimal trait value, but also in the diffusion variance under the Ornstein-Uhlenbeck (OU)…
A Generalization Bound of Deep Neural Networks for Dependent Data
Quan Huu Do, Binh T. Nguyen, Lam Si Tung Ho
Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications suc…
SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics
Esha Saha, Lam Si Tung Ho, Giang Tran
Predicting the evolution of diseases is challenging, especially when the data availability is scarce and incomplete. The most popular tools for modelling and predicting infectious…
Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy
Cuong N. Nguyen, Lam Si Tung Ho, Vu Dinh +2
We analyze new generalization bounds for deep learning models trained by transfer learning from a source to a target task. Our bounds utilize a quantity called the majority predict…
When can we reconstruct the ancestral state? Beyond Brownian motion
Nhat L. Vu, Thanh P. Nguyen, Binh T. Nguyen +2
Reconstructing the ancestral state of a group of species helps answer many important questions in evolutionary biology. Therefore, it is crucial to understand when we can estimate…
Evolutionary shift detection with ensemble variable selection
Wensha Zhang, Toby Kenney, Lam Si Tung Ho
1. Abrupt environmental changes can lead to evolutionary shifts in trait evolution. Identifying these shifts is an important step in understanding the evolutionary history of pheno…
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…
Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data
Lam Si Tung Ho, Nicholas Richardson, Giang Tran
In this paper, we propose an adaptive group Lasso deep neural network for high-dimensional function approximation where input data are generated from a dynamical system and the tar…
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…
Ancestral state reconstruction with large numbers of sequences and edge-length estimation
Lam Si Tung Ho, Edward Susko
Likelihood-based methods are widely considered the best approaches for reconstructing ancestral states. Although much effort has been made to study properties of these methods, pre…
Consistent Feature Selection for Analytic Deep Neural Networks
Vu Dinh, Lam Si Tung Ho
One of the most important steps toward interpretability and explainability of neural network models is feature selection, which aims to identify the subset of relevant features. Th…
Consistent feature selection for neural networks via Adaptive Group Lasso
Vu Dinh, Lam Si Tung Ho
One main obstacle for the wide use of deep learning in medical and engineering sciences is its interpretability. While neural network models are strong tools for making predictions…
Efficient Bayesian Inference of General Gaussian Models on Large Phylogenetic Trees
Paul Bastide, Lam Si Tung Ho, Guy Baele +2
Phylogenetic comparative methods correct for shared evolutionary history among a set of non-independent organisms by modeling sample traits as arising from a diffusion process alon…
Inferring phenotypic trait evolution on large trees with many incomplete measurements
Gabriel Hassler, Max R. Tolkoff, William L. Allen +3
Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships,…
Bayesian Active Learning With Abstention Feedbacks
Cuong V. Nguyen, Lam Si Tung Ho, Huan Xu +2
We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important pr…
On the convergence of the maximum likelihood estimator for the transition rate under a 2-state symmetric model
Lam Si Tung Ho, Vu Dinh, Frederick A. Matsen +1
Maximum likelihood estimators are used extensively to estimate unknown parameters of stochastic trait evolution models on phylogenetic trees. Although the MLE has been proven to co…