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20122023
most citedBayesian Nonparametric Federated Learning of Neural Networks

147 citations · 233 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.LG2023

Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches

Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen +1

Randomized smoothing has recently attracted attentions in the field of adversarial robustness to provide provable robustness guarantees on smoothed neural network classifiers. Howe…

cs.LG2023

Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization

Alireza Ghods, Trong Nghia Hoang, Diane Cook

Data summarization is the process of generating interpretable and representative subsets from a dataset. Existing time series summarization approaches often search for recurring su…

cs.LG2021

Adaptive Multi-Source Causal Inference

Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang +1

Data scarcity is a tremendous challenge in causal effect estimation. In this paper, we propose to exploit additional data sources to facilitate estimating causal effects in the tar…

cs.LG20201 cited

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes

Quang Minh Hoang, Trong Nghia Hoang, Hai Pham +1

We introduce a new scalable approximation for Gaussian processes with provable guarantees which hold simultaneously over its entire parameter space. Our approximation is obtained f…

cs.LG2020

CHEER: Rich Model Helps Poor Model via Knowledge Infusion

Cao Xiao, Trong Nghia Hoang, Shenda Hong +2

There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e.g., intensi…

cs.LG201913 cited

CASTER: Predicting Drug Interactions with Chemical Substructure Representation

Kexin Huang, Cao Xiao, Trong Nghia Hoang +2

Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality. Identifying potential DDIs during the drug design process is critical for patients and soci…