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Causal Discovery by Kernel Deviance Measures with Heterogeneous Transforms
To go about capturing these discrepancies between cause and effect remains to be a challenge and many current state-of-the-art algorithms propose to compare the norms of the kernel mean embeddings of the conditional distributions.
Causal Coordinated Concurrent Reinforcement Learning
In this work, we propose a novel algorithmic framework for data sharing and coordinated exploration for the purpose of learning more data-efficient and better performing policies under a concurrent reinforcement learning (CRL) setting.
Causal Discovery by Kernel Intrinsic Invariance Measure
We show our algorithm can be reduced to an eigen-decomposition task on a kernel matrix measuring intrinsic deviance/invariance.
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