Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport
Johns Hopkins University（约翰斯·霍普金斯大学）
Adarsh Subbaswamy, Peter Schulam, Suchi Saria
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In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. Previously presented at the NeurIPS 2018 Causal Learning Workshop
Classical supervised learning produces unreliable models when training and target distributions differ, with most existing solutions requiring samples from the target domain. We propose a proactive approach which learns a relationship in the training domain that will generalize to the target domain by incorporating prior knowledge of aspects of the data generating process that are expected to differ as expressed in a causal selection diagram. Specifically, we remove variables generated by unstable mechanisms from the joint factorization to yield the Surgery Estimator---an interventional distribution that is invariant to the differences across environments. We prove that the surgery estimator finds stable relationships in strictly more scenarios than previous approaches which only consider conditional relationships, and demonstrate this in simulated experiments. We also evaluate on real world data for which the true causal diagram is unknown, performing competitively against entirely data-driven approaches.
Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset
Lamarr Institute for Machine Learning and Artificial Intelligence, TU Dortmund University（拉马尔机器学习与人工智能研究所，多特蒙德工业大学）；Faculty of Economics - University of Porto（波尔图大学经济学院）
Rochester Institute of Technology（罗切斯特理工学院）；Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences（亥姆霍兹波茨坦中心——德国地球科学研究中心）；University of Potsdam（波茨坦大学）；University of Padova（帕多瓦大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）
Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
Jagiellonian Human-Centered AI Lab, Mark Kac Center for Complex Systems Research, Institute of Applied Computer Science, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University（雅盖隆大学人中心人工智能实验室、马克·卡中心复杂系统研究所以及应用计算机科学研究所、物理、天文学与应用计算机科学学院）；Institute of Computing Science, Poznań University of Technology（波兹南技术大学计算科学研究所）
