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Found 8 papers, 2 papers with code
Federated Learning of Causal Effects from Incomplete Observational Data
We introduce a new approach for federated causal inference from incomplete data, enabling the estimation of causal effects from multiple decentralized and incomplete data sources.
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting.
Adaptive Multi-Source Causal Inference
The proposed method can infer causal effects in the target population without prior knowledge of data discrepancy between the additional data sources and the target.
Federated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed.
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection
This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014).
Subdomain Adaptation with Manifolds Discrepancy Alignment
Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains.
Causal Modeling with Stochastic Confounders
This work extends causal inference with stochastic confounders.
Knowledge-driven generative subspaces for modeling multi-view dependencies in medical data
Early detection of Alzheimer's disease (AD) and identification of potential risk/beneficial factors are important for planning and administering timely interventions or preventive measures.
