4 citations · 15 across the 21 of their papers we have counts for
23 papers
Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging
Milad Masroor, Cuong Nguyen, Kevin Wells +1
Medical image analysis models can exhibit performance disparities across patient subgroups, threatening clinical safety and fairness. Existing methods typically address this issue…
Multi-agent decision making: A Blackwell's informativeness approach
Zheng Zhang, Cuong C. Nguyen, Kevin Wells +1
The rapid development of large language models (LLMs) has motivated research on decision-making in multi-agent systems, where multiple agents collaborate to achieve shared objectiv…
Fatigue-Aware Learning to Defer via Constrained Optimisation
Zheng Zhang, Cuong C. Nguyen, David Rosewarne +2
Learning to defer (L2D) enables human-AI cooperation by deciding when an AI system should act autonomously or defer to a human expert. Existing L2D methods, however, assume static…
People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation
Zheng Zhang, Milad Masroor, Cuong Nguyen +6
Machine learning models for medical image analysis often exhibit subgroup-dependent performance, which impacts how decisions should be allocated between automated systems and human…
Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models
Dung Anh Hoang, Cuong Pham, Cuong Nguyen +3
Large Language Models (LLMs) deliver strong performance across a wide range of NLP tasks, but their massive sizes hinder deployment on resource-constrained devices. To reduce their…
Adaptive Layer-Wise Transformations for Post-Training Quantization of Large Language Models
Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen +4
Large language models require significant computational resources for deployment, making quantization essential for practical applications. However, the main obstacle to effective…
Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models
Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen +3
Large language models (LLMs) have significantly advanced natural language processing, but their massive parameter counts create substantial computational and memory challenges duri…
AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning
Arpit Garg, Cuong Nguyen, Rafael Felix +3
Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets oft…
Coverage-Constrained Human-AI Cooperation with Multiple Experts
Zheng Zhang, Cuong Nguyen, Kevin Wells +3
Human-AI cooperative classification (HAI-CC) approaches aim to develop hybrid intelligent systems that enhance decision-making in various high-stakes real-world scenarios by levera…
MetaAug: Meta-Data Augmentation for Post-Training Quantization
Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen +4
Post-Training Quantization (PTQ) has received significant attention because it requires only a small set of calibration data to quantize a full-precision model, which is more pract…
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Cuong Pham, Cuong C. Nguyen, Trung Le +3
Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared…
Learning to Complement with Multiple Humans
Zheng Zhang, Cuong Nguyen, Kevin Wells +2
Real-world image classification tasks tend to be complex, where expert labellers are sometimes unsure about the classes present in the images, leading to the issue of learning with…
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Deep learning faces a formidable challenge when handling noisy labels, as models tend to overfit samples affected by label noise. This challenge is further compounded by the presen…
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
Arpit Garg, Cuong Nguyen, Rafael Felix +2
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with no…
Task Weighting in Meta-learning with Trajectory Optimisation
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
Developing meta-learning algorithms that are un-biased toward a subset of training tasks often requires hand-designed criteria to weight tasks, potentially resulting in sub-optimal…
Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
Learning from noisy labels (LNL) is crucial in deep learning, in which one of the approaches is to identify clean-label samples from poorly-annotated datasets. Such an identificati…
Instance-Dependent Noisy Label Learning via Graphical Modelling
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them. There are many types of label noise, such as symmetric, asymm…
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
Dung Anh Hoang, Cuong Nguyen, Belagiannis Vasileios +2
Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced…
Probabilistic task modelling for meta-learning
Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro
We propose probabilistic task modelling -- a generative probabilistic model for collections of tasks used in meta-learning. The proposed model combines variational auto-encoding an…
Similarity of Classification Tasks
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
Recent advances in meta-learning has led to remarkable performances on several few-shot learning benchmarks. However, such success often ignores the similarity between training and…
PAC-Bayes meta-learning with implicit task-specific posteriors
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning. Our proposed method extends the PAC-Bayes framework from a single task…
Unsupervised Task Design to Meta-Train Medical Image Classifiers
Gabriel Maicas, Cuong Nguyen, Farbod Motlagh +2
Meta-training has been empirically demonstrated to be the most effective pre-training method for few-shot learning of medical image classifiers (i.e., classifiers modeled with smal…
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algor…