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A Causal View on Robustness of Neural Networks
Cheng Zhang · Kun Zhang · Yingzhen Li

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #805

We present a causal view on the robustness of neural networks against input manipulations, which applies not only to traditional classification tasks but also to general measurement data. Based on this view, we design a deep causal manipulation augmented model (deep CAMA) which explicitly models possible manipulations on certain causes leading to changes in the observed effect. We further develop data augmentation and test-time fine-tuning methods to improve deep CAMA's robustness. When compared with discriminative deep neural networks, our proposed model shows superior robustness against unseen manipulations. As a by-product, our model achieves disentangled representation which separates the representation of manipulations from those of other latent causes.

Author Information

Cheng Zhang (Microsoft Research, Cambridge, UK)

Cheng Zhang is a principal researcher at Microsoft Research Cambridge, UK. She leads the Data Efficient Decision Making (Project Azua) team in Microsoft. Before joining Microsoft, she was with the statistical machine learning group of Disney Research Pittsburgh, located at Carnegie Mellon University. She received her Ph.D. from the KTH Royal Institute of Technology. She is interested in advancing machine learning methods, including variational inference, deep generative models, and sequential decision-making under uncertainty; and adapting machine learning to social impactful applications such as education and healthcare. She co-organized the Symposium on Advances in Approximate Bayesian Inference from 2017 to 2019.

Kun Zhang (CMU)
Yingzhen Li (Microsoft Research Cambridge)

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