118 citations · 235 across the 20 of their papers we have counts for
20 papers
Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models
Duc Anh Nguyen, Tien Ngoc Luu, Tung Pham +1
State-based fine-tuning has emerged as a compelling alternative to weight-based adaptation for transformers, updating lightweight controls into states rather than model weights, of…
TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
Siyu Li, Toan Tran, Lingyi Zhao +2
Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating…
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
Toan Tran, Waqwoya Abebe, Abhishek Potnis +4
This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Successful geolocalization can pro…
Differentially Private Synthetic Data via APIs 4: Tabular Data
Toan Tran, Arturs Backurs, Zinan Lin +3
This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains. Despite extensive st…
Selective Sinkhorn Routing for Improved Sparse Mixture of Experts
Duc Anh Nguyen, Huu Binh Ta, Nhuan Le Duc +2
Sparse Mixture-of-Experts (SMoE) models are scalable and computationally efficient, enabling large increases in model capacity with limited inference overhead. Existing SMoE method…
Geo-Llama: Leveraging LLMs for Human Mobility Trajectory Generation with Spatiotemporal Constraints
Siyu Li, Toan Tran, Haowen Lin +5
Generating realistic human mobility data is essential for various application domains, including transportation, urban planning, and epidemic control, as real data is often inacces…
Differentially Private Tabular Data Synthesis using Large Language Models
Toan V. Tran, Li Xiong
Synthetic tabular data generation with differential privacy is a crucial problem to enable data sharing with formal privacy. Despite a rich history of methodological research and d…
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All
Quyen Tran, Hoang Phan, Lam Tran +4
Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field o…
Generalization Bounds for Robust Contrastive Learning: From Theory to Practice
Ngoc N. Tran, Lam Tran, Hoang Phan +5
Contrastive Learning first extracts features from unlabeled data, followed by linear probing with labeled data. Adversarial Contrastive Learning (ACL) integrates Adversarial Traini…
Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology
Tuong Do, Binh X. Nguyen, Vuong Pham +4
Federated learning is an active research topic since it enables several participants to jointly train a model without sharing local data. Currently, cross-silo federated learning i…
Stochastic Multiple Target Sampling Gradient Descent
Hoang Phan, Ngoc Tran, Trung Le +3
Sampling from an unnormalized target distribution is an essential problem with many applications in probabilistic inference. Stein Variational Gradient Descent (SVGD) has been show…
Distributionally Robust Fair Principal Components via Geodesic Descents
Hieu Vu, Toan Tran, Man-Chung Yue +1
Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines. In consequential domains such as college admission, hea…
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Trung Phung, Trung Le, Long Vuong +4
Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e.g., learning domain-invariant representations a…
Exploiting Domain-Specific Features to Enhance Domain Generalization
Manh-Ha Bui, Toan Tran, Anh Tuan Tran +1
Domain Generalization (DG) aims to train a model, from multiple observed source domains, in order to perform well on unseen target domains. To obtain the generalization capability,…
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…
Domain Invariant Representation Learning with Domain Density Transformations
A. Tuan Nguyen, Toan Tran, Yarin Gal +1
Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains. Naively tr…
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Anh Tong, Toan Tran, Hung Bui +1
Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitne…
Bayesian Generative Active Deep Learning
Toan Tran, Thanh-Toan Do, Ian Reid +1
Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources…
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning
Thanh-Toan Do, Toan Tran, Ian Reid +3
We propose a method that substantially improves the efficiency of deep distance metric learning based on the optimization of the triplet loss function. One epoch of such training p…
A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran, Trung Pham, Gustavo Carneiro +2
Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on…