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Causal Matrix Completion
October 1, 2021 @ 11:00 am - 12:00 pm
Devavrat Shah (MIT)
Abstract: Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are “missing completely atrandom” (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of “latent confounders”, i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the observed matrix.
This is based on joint works with Anish Agarwal (MIT), Munther Dahleh (MIT) and Dennis Shen (UC Berkeley).
Bio: Devavrat Shah is the Andrew (1956) and Erna Viterbi Professor with the department of electrical engineering and computer science, MIT. He is a member of LIDS and the ORC, and the Faculty Director of the MicroMasters in Statistics and Data Science program at IDSS. His research focus is on theory of large complex networks, which includes network algorithms, stochastic networks, network information theory and large-scale statistical inference.