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Interventional Causal Representation Learning
Kartik Ahuja · Yixin Wang · Divyat Mahajan · Yoshua Bengio
Event URL: https://openreview.net/forum?id=rMCXjkqITMI »

The theory of identifiable representation learning aims to build general-purpose methods that extract high-level latent (causal) factors from low-level sensory data. Most existing works focus on identifiable representation learning with observational data, relying on distributional assumptions on latent (causal) factors. However, in practice, we often also have access to interventional data for representation learning. How can we leverage interventional data to help identify high-level latents? To this end, we explore the role of interventional data for identifiable representation learning in this work. We study the identifiability of latent causal factors with and without interventional data, under minimal distributional assumptions on the latents. We prove that, if the true latent variables map to the observed high-dimensional data via a polynomial function, then representation learning via minimizing the standard reconstruction loss of autoencoders identifies the true latents up to affine transformation. If we further have access to interventional data generated by hard do interventions on some of the latents, then we can identify these intervened latents up to permutation, shift and scaling.

Author Information

Kartik Ahuja (Mila)
Yixin Wang (University of Michigan)
Divyat Mahajan (Microsoft Research)

I am a Research Fellow at Microsoft Research Lab India, where I work with Amit Sharma on Machine Learning and Causal Inference. Prior to joining MSR, I completed my undergraduate double major program in Mathematics and Computer Science from the Indian Institute of Technology, Kanpur. Interests: Machine Learning | Causal Inference | Explainability, Generalization and Robustness in Deep Learning

Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

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