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PAC Generalization via Invariant Representations
Advait Parulekar · Karthikeyan Shanmugam · Sanjay Shakkottai

Tue Jul 25 02:00 PM -- 04:30 PM (PDT) @ Exhibit Hall 1 #119
Invariant representations are transformations of the covariates such that the best model on top of the representation is invariant across training environments. In the context of linear Structural Equation Models (SEMs), invariant representations might allow us to learn models with out-of-distribution guarantees, i.e., models that are robust to interventions in the SEM. To address the invariant representation problem in a *finite sample* setting, we consider the notion of $\epsilon$-approximate invariance. We study the following question: If a representation is approximately invariant with respect to a given number of training interventions, will it continue to be approximately invariant on a larger collection of unseen intervened SEMs? Inspired by PAC learning, we obtain finite-sample out-of-distribution generalization guarantees for approximate invariance that holds *probabilistically* over a family of linear SEMs without faithfulness assumptions.

Author Information

Advait Parulekar (University of Texas at Austin)
Karthikeyan Shanmugam (IBM Research NY)

I am currently a Research Staff Member with the IBM Research AI group, NY since 2017. Previously, I was a Herman Goldstine Postdoctoral Fellow in the Math Sciences Division at IBM Research, NY. I obtained my Ph.D. in Electrical and Computer Engineering from UT Austin in summer 2016. My advisor at UT was Alex Dimakis. I obtained my MS degree in Electrical Engineering (2010-2012) from the University of Southern California, B.Tech and M.Tech degrees in Electrical Engineering from IIT Madras in 2010. My research interests broadly lie in Graph algorithms, Machine learning, Optimization, Coding Theory and Information Theory. In machine learning, my recent focus is on graphical model learning, causal inference and explainability. I also work on problems relating to information flow, storage and caching over networks.

Sanjay Shakkottai (University of Texas at Austin)

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