280 citations · 467 across the 15 of their papers we have counts for
18 papers
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…
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…
EnSolver: Uncertainty-Aware Ensemble CAPTCHA Solvers with Theoretical Guarantees
Duc C. Hoang, Behzad Ousat, Amin Kharraz +1
The popularity of text-based CAPTCHA as a security mechanism to protect websites from automated bots has prompted researches in CAPTCHA solvers, with the aim of understanding its f…
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Matthew Ashman, Thang D. Bui, Cuong V. Nguyen +4
The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional alg…
LEEP: A New Measure to Evaluate Transferability of Learned Representations
Cuong V. Nguyen, Tal Hassner, Matthias Seeger +1
We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy…
Transferability and Hardness of Supervised Classification Tasks
Anh T. Tran, Cuong V. Nguyen, Tal Hassner
We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does n…
Toward Understanding Catastrophic Forgetting in Continual Learning
Cuong V. Nguyen, Alessandro Achille, Michael Lam +3
We study the relationship between catastrophic forgetting and properties of task sequences. In particular, given a sequence of tasks, we would like to understand which properties o…
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…
Improving and Understanding Variational Continual Learning
Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui +1
In the continual learning setting, tasks are encountered sequentially. The goal is to learn whilst i) avoiding catastrophic forgetting, ii) efficiently using model capacity, and ii…
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop +1
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. However, practitioners are faced with a fragmented literature that offers a…
Variational Continual Learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui +1
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in…
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…
Streaming Sparse Gaussian Process Approximations
Thang D. Bui, Cuong V. Nguyen, Richard E. Turner
Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractab…
Accelerated Randomized Mirror Descent Algorithms For Composite Non-strongly Convex Optimization
Le Thi Khanh Hien, Cuong V. Nguyen, Huan Xu +2
We consider the problem of minimizing the sum of an average function of a large number of smooth convex components and a general, possibly non-differentiable, convex function. Alth…
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification
Nguyen Viet Cuong, Nan Ye, Wee Sun Lee
We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to…
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
Nguyen Viet Cuong, Huan Xu
We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learn…
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…