457 citations · 509 across the 18 of their papers we have counts for
20 papers
A first Kronecker limit formula for Kleinian groups
Zihan Miao, Anh Nguyen, Tian An Wong
We prove a first Kronecker limit formula for cofinite discrete subgroups of SL, also called Kleinian groups, generalizing a method of Goldstein over SL$(2,\mathbb R…
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…
Meet in the Middle: A New Pre-training Paradigm
Anh Nguyen, Nikos Karampatziakis, Weizhu Chen
Most language models (LMs) are trained and applied in an autoregressive left-to-right fashion, assuming that the next token only depends on the preceding ones. However, this assump…
A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals
Anh-Tu Nguyen, Thao Nguyen, Huy-Khiem Le +2
Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, an…
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…
Polychrony as Chinampas
Eric Dolores-Cuenca, Jose Antonio Arciniega-Nevarez, Anh Nguyen +5
In this paper, we study the flow of signals through linear paths with the nonlinear condition that a node emits a signal when it receives external stimuli or when two incoming sign…
Practical Cross-modal Manifold Alignment for Grounded Language
Andre T. Nguyen, Luke E. Richards, Gaoussou Youssouf Kebe +4
We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world it…
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…
Would a File by Any Other Name Seem as Malicious?
Andre T. Nguyen, Edward Raff, Aaron Sant-Miller
Successful malware attacks on information technology systems can cause millions of dollars in damage, the exposure of sensitive and private information, and the irreversible destru…
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…
Connecting Lyapunov Control Theory to Adversarial Attacks
Arash Rahnama, Andre T. Nguyen, Edward Raff
Significant work is being done to develop the math and tools necessary to build provable defenses, or at least bounds, against adversarial attacks of neural networks. In this work,…
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…
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
Mauricio Santillana, Andre T. Nguyen, Mark Dredze +2
We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple d…