30 citations · 59 across the 20 of their papers we have counts for
6 papers · 1 filter
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…
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…
Bayesian Pool-based 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 p…
Fast learning rates with heavy-tailed losses
Vu Dinh, Lam Si Tung Ho, Duy Nguyen +1
We study fast learning rates when the losses are not necessarily bounded and may have a distribution with heavy tails. To enable such analyses, we introduce two new conditions: (i)…
Learning From Non-iid Data: Fast Rates for the One-vs-All Multiclass Plug-in Classifiers
Vu Dinh, Lam Si Tung Ho, Nguyen Viet Cuong +2
We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging dri…
Generalization and Robustness of Batched Weighted Average Algorithm with V-geometrically Ergodic Markov Data
Nguyen Viet Cuong, Lam Si Tung Ho, Vu Dinh
We analyze the generalization and robustness of the batched weighted average algorithm for V-geometrically ergodic Markov data. This algorithm is a good alternative to the empirica…