English(EN)SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery
新研究推动AI可解释性和因果发现方法
作者PulseAugur 编辑部·[8 个来源]·
研究人员正在开发机器学习中的因果发现和推理的先进方法，旨在提高AI模型的可解释性和可靠性。一篇论文提出在布尔结构因果模型中使用Halpern-Pearl实际原因，为神经网络预测提供更准确的解释，在可扩展性和准确性方面优于现有基线。另一个框架GENESIS专注于可解释的因果发现，通过确保有向无环图中每个边的决策都可以追溯到可审计的证据，实现了100%的决策可追溯性，同时保持了具有竞争力的性能。此外，一个新的时空框架将近因因果推理扩展到处理隐藏混淆和干扰，利用基于Transformer的编码器进行识别。最后，引入了大型因果模型（LCMs）的概念，作为时间因果发现的基础模型范式，展示了有效的扩展和泛化能力。
AI
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arXiv cs.AI
TIER_1English(EN)·Jannick Strobel, Muqsit Azeem, Stefan Leue·
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arXiv cs.LG
TIER_1(CA)·Nikolaos Kougioulis, Nikolaos Gkorgkolis, MingXue Wang, Bora Caglayan, Dario Simionato, Andrea Tonon, Ioannis Tsamardinos·
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arXiv stat.ML
TIER_1English(EN)·Marios Andreou, Nan Chen·
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