8 citations · 9 across the 10 of their papers we have counts for
12 papers
Improving Category-Aware Active Domain Adaptation via Uncertainty-Guided Region Coverage
My Tra Nguyen Huynh, Viet Cuong Ta
Active domain adaptation researches focus on alleviating the annotation cost, which comprises source data and target data labelling, as well as enhancing the performance of unsuper…
Low Variance Trust Region Optimization with Independent Actors and Sequential Updates in Cooperative Multi-agent Reinforcement Learning
Bang Giang Le, Viet Cuong Ta
Cooperative multi-agent reinforcement learning assumes each agent shares the same reward function and can be trained effectively using the Trust Region framework of single-agent. I…
Balancing Knowledge Distillation for Imbalance Learning with Bilevel Optimization
Anh B. H. Nguyen, Ba Tho Phan, Viet Cuong Ta
Knowledge distillation transfers knowledge from a high capacity teacher to a compact student using a mixture of hard and soft losses. On imbalanced data, a fixed weighting between…
Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning
Ba Hoang Anh Nguyen, Viet Cuong Ta
Training a deep neural network with noisy labels could reduce data annotation cost but may introduce noise into the learned model. In meta label correction approaches, an additiona…
Sequence Diffusion Model for Temporal Link Prediction in Continuous-Time Dynamic Graph
Nguyen Minh Duc, Viet Cuong Ta
Temporal link prediction in dynamic graphs is a fundamental problem in many real-world systems. Existing temporal graph neural networks mainly focus on learning representations of…
Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learning
Bang Giang Le, Viet Cuong Ta
To promote cooperation in Multi-Agent Reinforcement Learning, the reward signals of all agents can be aggregated together, forming global rewards that are commonly known as the ful…
Resolve Highway Conflict in Multi-Autonomous Vehicle Controls with Local State Attention
Xuan Duy Ta, Bang Giang Le, Thanh Ha Le +1
In mixed-traffic environments, autonomous vehicles must adapt to human-controlled vehicles and other unusual driving situations. This setting can be framed as a multi-agent reinfor…
Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning
Duc Kien Doan, Bang Giang Le, Viet Cuong Ta
In safe reinforcement learning, agent needs to balance between exploration actions and safety constraints. Following this paradigm, domain transfer approaches learn a prior Q-funct…
Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning
Bang Giang Le, Viet Cuong Ta
In this work, we study the problem of finding Pareto optimal policies in multi-agent reinforcement learning problems with cooperative reward structures. We show that any algorithm…
Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation
Thi Linh Hoang, Tuan Dung Pham, Viet Cuong Ta
In this work, we have proposed an approach for improving the GCN for predicting ratings in social networks. Our model is expanded from the standard model with several layers of tra…
Distill Knowledge in Multi-task Reinforcement Learning with Optimal-Transport Regularization
Bang Giang Le, Viet Cuong Ta
In multi-task reinforcement learning, it is possible to improve the data efficiency of training agents by transferring knowledge from other different but related tasks. Because the…
Smartphone-based user positioning in a multiple-user context with Wi-Fi and Bluetooth
Viet-Cuong Ta, Trung-Kien Dao, Dominique Vaufreydaz +1
In a multiuser context, the Bluetooth data from the smartphone could give an approximation of the distance between users. Meanwhile, the Wi-Fi data can be used to calculate the use…