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Publications [#341908] of Sayan Mukherjee

Papers Published

  1. Tan, Z; Roche, K; Zhou, X; Mukherjee, S, Scalable algorithms for learning high-dimensional linear mixed models, 34th Conference on Uncertainty in Artificial Intelligence 2018, Uai 2018, vol. 1 (January, 2018), pp. 259-268, ISBN 9781510871601
    (last updated on 2019/03/27)

    © 2018 by Association For Uncertainty in Artificial Intelligence (AUAI) All rights reserved. Linear mixed models (LMMs) are used extensively to model observations that are not independent. Parameter estimation for LMMs can be computationally prohibitive on big data. State-of-the-art learning algorithms require computational complexity which depends at least linearly on the dimension p of the covariates, and often use heuristics that do not offer theoretical guarantees. We present scalable algorithms for learning high-dimensional LMMs with sublinear computational complexity dependence on p. Key to our approach are novel dual estimators which use only kernel functions of the data, and fast computational techniques based on the subsampled randomized Hadamard transform. We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation. Finally, we complement the theory with experiments on large synthetic and real data.
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