457 citations · 509 across the 18 of their papers we have counts for
12 papers · 1 filter
CASUAL: Conditional Support Alignment for Domain Adaptation with Label Shift
Anh T Nguyen, Lam Tran, Anh Tong +2
Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabeled on…
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training
Derek Everett, Andre T. Nguyen, Luke E. Richards +1
The quantification of uncertainty is important for the adoption of machine learning, especially to reject out-of-distribution (OOD) data back to human experts for review. Yet progr…
Task-Agnostic Robust Representation Learning
A. Tuan Nguyen, Ser Nam Lim, Philip Torr
It has been reported that deep learning models are extremely vulnerable to small but intentionally chosen perturbations of its input. In particular, a deep network, despite its nea…
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
Andre T. Nguyen, Fred Lu, Gary Lopez Munoz +3
We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how pre…
Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization
Thanh Nguyen-Tang, Sunil Gupta, A. Tuan Nguyen +1
Offline policy learning (OPL) leverages existing data collected a priori for policy optimization without any active exploration. Despite the prevalence and recent interest in this…
Adversarial Transfer Attacks With Unknown Data and Class Overlap
Luke E. Richards, André Nguyen, Ryan Capps +3
The ability to transfer adversarial attacks from one model (the surrogate) to another model (the victim) has been an issue of concern within the machine learning (ML) community. Th…
Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints
Andre T. Nguyen, Edward Raff, Charles Nicholas +1
The detection of malware is a critical task for the protection of computing environments. This task often requires extremely low false positive rates (FPR) of 0.01% or even lower,…
KL Guided Domain Adaptation
A. Tuan Nguyen, Toan Tran, Yarin Gal +2
Domain adaptation is an important problem and often needed for real-world applications. In this problem, instead of i.i.d. training and testing datapoints, we assume that the sourc…
Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions
Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana
We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Int…
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
Arash Rahnama, Andre T. Nguyen, Edward Raff
Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to the input. These adversarial perturbations, though imperceptible, can easily mislead the D…
Heterogeneous Relational Kernel Learning
Andre T. Nguyen, Edward Raff
Recent work has developed Bayesian methods for the automatic statistical analysis and description of single time series as well as of homogeneous sets of time series data. We exten…
Adversarial Attacks, Regression, and Numerical Stability Regularization
Andre T. Nguyen, Edward Raff
Adversarial attacks against neural networks in a regression setting are a critical yet understudied problem. In this work, we advance the state of the art by investigating adversar…