7 citations · 16 across the 13 of their papers we have counts for
14 papers
Federated Causal Inference from Observational Data
Thanh Vinh Vo, Young lee, Tze-Yun Leong
Decentralized data sources are prevalent in real-world applications, posing a formidable challenge for causal inference. These sources cannot be consolidated into a single entity o…
Adversarial Learning Networks: Source-free Unsupervised Domain Incremental Learning
Abhinit Kumar Ambastha, Leong Tze Yun
This work presents an approach for incrementally updating deep neural network (DNN) models in a non-stationary environment. DNN models are sensitive to changes in input data distri…
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
Thanh Vinh Vo, Arnab Bhattacharyya, Young Lee +1
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm th…
Federated Estimation of Causal Effects from Observational Data
Thanh Vinh Vo, Trong Nghia Hoang, Young Lee +1
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed. Till date, most insight into the causal inference requires data to be…
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…
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection
Xinyi Xu, Tiancheng Huang, Pengfei Wei +2
This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristi…