7 papers
Mitigating Privacy Risk via Forget Set-Free Unlearning
Aviraj Newatia, Michael Cooper, Viet Nguyen +1
Training machine learning models requires the storage of large datasets, which often contain sensitive or private data. Storing data is associated with a number of potential risks…
Quantum Patches: Enhancing Robustness of Quantum Machine Learning Models
Ban Q. Tran, Chuong K. Luong, Viet Q. Nguyen +2
Machine learning models and their applications, such as autonomous driving systems, are becoming increasingly common and are essential components of human daily life. However, due…
A Statistical Theory of Gated Attention through the Lens of Hierarchical Mixture of Experts
Viet Nguyen, Tuan Minh Pham, Thinh Cao +4
Self-attention has greatly contributed to the success of the widely used Transformer architecture by enabling learning from data with long-range dependencies. In an effort to impro…
Rethinking Multinomial Logistic Mixture of Experts with Sigmoid Gating Function
Tuan Minh Pham, Thinh Cao, Viet Nguyen +3
The sigmoid gate in mixture-of-experts (MoE) models has been empirically shown to outperform the softmax gate across several tasks, ranging from approximating feed-forward networks…
Reliably Detecting Model Failures in Deployment Without Labels
Viet Nguyen, Changjian Shui, Vijay Giri +4
The distribution of data changes over time; models operating in dynamic environments need retraining. But knowing when to retrain, without access to labels, is an open challenge si…
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen +2
We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information. These dem…