Mel-frequency Cepstral Coefficients for Eye Movement Identification. - Viet Cuong Nguyen; Vu C. Dinh; Lam Si Tung Ho (ICTAI, 2012)

Generalization and Robustness of Batched Weighted Average Algorithm with V-Geometrically Ergodic Markov Data. - Viet Cuong Nguyen; Lam Si Tung Ho; Vu C. Dinh (ALT, 2013)

Phase transition on the convergence rate of parameter estimation under an Ornstein-Uhlenbeck diffusion on a tree. - Cécile Ané; Lam Si Tung Ho; Sebastien Roch (CoRR, 2014)

Learning from Non-iid Data: Fast Rates for the One-vs-All Multiclass Plug-in Classifiers. - Vu C. Dinh; Lam Si Tung Ho; Viet Cuong Nguyen; Duy M. H. Nguyen; Binh T. Nguyen 001 (TAMC, 2015)

Bounds on the Expected Size of the Maximum Agreement Subtree. - Daniel Irving Bernstein; Lam Si Tung Ho; Colby Long; Mike A. Steel; Katherine St. John; Seth Sullivant (SIAM J. Discret. Math., 2015)

Fast learning rates with heavy-tailed losses. - Vu C. Dinh; Lam Si Tung Ho; Binh T. Nguyen 001; Duy M. H. Nguyen (NIPS, 2016)

OASIS: An Active Framework for Set Inversion. - Binh T. Nguyen 001; Duy M. H. Nguyen; Lam Si Tung Ho; Vu C. Dinh (SoMeT, 2018)

Recovery guarantees for polynomial approximation from dependent data with outliers. - Lam Si Tung Ho; Hayden Schaeffer; Giang Tran; Rachel A. Ward (CoRR, 2018)

An active learning framework for set inversion. - Binh T. Nguyen 001; Duy M. H. Nguyen; Lam Si Tung Ho; Vu C. Dinh (Knowl. Based Syst., 2019)

Consistent feature selection for analytic deep neural networks. - Vu C. Dinh; Lam Si Tung Ho (NeurIPS, 2020)

Consistent feature selection for neural networks via Adaptive Group Lasso. - Lam Si Tung Ho; Vu C. Dinh (CoRR, 2020)

Posterior concentration and fast convergence rates for generalized Bayesian learning. - Lam Si Tung Ho; Binh T. Nguyen 001; Vu C. Dinh; Duy M. H. Nguyen (Inf. Sci., 2020)

Recovery guarantees for polynomial coefficients from weakly dependent data with outliers. - Lam Si Tung Ho; Hayden Schaeffer; Giang Tran; Rachel A. Ward (J. Approx. Theory, 2020)

Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data. - Lam Si Tung Ho; Giang Tran (CoRR, 2021)

Searching for Minimal Optimal Neural Networks. - Lam Si Tung Ho; Vu C. Dinh (CoRR, 2021)

Posterior concentration and fast convergence rates for generalized Bayesian learning. - Lam Si Tung Ho; Binh T. Nguyen 001; Vu C. Dinh; Duy Nguyen 003 (CoRR, 2021)

Generalization Bounds for Deep Transfer Learning Using Majority Predictor Accuracy. - Cuong N. Nguyen; Lam Si Tung Ho; Vu C. Dinh; Tal Hassner; Cuong V. Nguyen (ISITA, 2022)

SPADE4: Sparsity and Delay Embedding based Forecasting of Epidemics. - Esha Saha; Lam Si Tung Ho; Giang Tran (CoRR, 2022)

Bayesian active learning with abstention feedbacks. - Cuong V. Nguyen; Lam Si Tung Ho; Huan Xu 001; Vu C. Dinh; Binh T. Nguyen 001 (Neurocomputing, 2022)

Simple Transferability Estimation for Regression Tasks. - Cuong N. Nguyen; Phong Tran; Lam Si Tung Ho; Vu C. Dinh; Anh T. Tran; Tal Hassner; Cuong V. Nguyen (UAI, 2023)

A Generalization Bound of Deep Neural Networks for Dependent Data. - Quan Huu Do; Binh T. Nguyen 001; Lam Si Tung Ho (CoRR, 2023)

Simulation-Based Methods for Optimal Sampling Design in Systems Biology. - Tuan Minh Ha; Binh Thanh Nguyen; Lam Si Tung Ho (KSE, 2024)

Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient. - Vu C. Dinh; Lam Si Tung Ho; Cuong V. Nguyen (CoRR, 2024)