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Poster
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness
Raphael Suter · Djordje Miladinovic · Bernhard Schölkopf · Stefan Bauer

Thu Jun 13 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #29
in Posters Thu »

The ability to learn disentangled representations that split underlying sources of variation in high dimensional, unstructured data is important for data efficient and robust use of neural networks. While various approaches aiming towards this goal have been proposed in recent times, a commonly accepted definition and validation procedure is missing. We provide a causal perspective on representation learning which covers disentanglement and domain shift robustness as special cases. Our causal framework allows us to introduce a new metric for the quantitative evaluation of deep latent variable models. We show how this metric can be estimated from labeled observational data and further provide an efficient estimation algorithm that scales linearly in the dataset size.

Keywords: Causality · Representation Learning

Author Information

Raphael Suter (ETH Zurich)
Djordje Miladinovic (ETH Zurich)
Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

Stefan Bauer (MPI for Intelligent Systems)

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