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Transportable Representations for Out-of-distribution Generalization
Amirkasra Jalaldoust · Elias Bareinboim
Event URL: https://openreview.net/forum?id=govUPD2rlM »

Building on the theory of causal transportability (Bareinboim & Pearl), we define in this paper the notion of ``transportable representations," and show that the out-of-distribution generalization risk of classifiers defined based on these representations can be bounded, considering that graphical assumptions about the underlying system are provided.

Author Information

Amirkasra Jalaldoust (Columbia University)
Elias Bareinboim (Columbia University)
Elias Bareinboim

Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence (CausalAI) Laboratory at Columbia University. His research focuses on causal and counterfactual inference and their applications to artificial intelligence and machine learning as well as data-driven fields in the health and social sciences. His work was the first to propose a general solution to the problem of "causal data-fusion," providing practical methods for combining datasets generated under different experimental conditions and plagued with various biases. In the last years, Bareinboim has been exploring the intersection of causal inference with decision-making (including reinforcement learning) and explainability (including fairness analysis). Before joining Columbia, he was an assistant professor at Purdue University and received his Ph.D. in Computer Science from the University of California, Los Angeles. Bareinboim was named one of ``AI's 10 to Watch'' by IEEE, and is a recipient of an NSF CAREER Award, the Dan David Prize Scholarship, the 2014 AAAI Outstanding Paper Award, and the 2019 UAI Best Paper Award.

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