Generalized Linear Model

Wednesday, December 5, 2018 - 1:30pm - 2:30pm
Mike Wei (University at Buffalo (SUNY))
We propose a minimax concave penalized multi-armed bandit algorithm under generalized linear model (G-MCP-Bandit) for a decision-maker facing high-dimensional data in an online learning and decision-making process. We demonstrate that the G-MCP-Bandit algorithm asymptotically achieves the optimal cumulative regret in the sample size dimension T, O(log T), and further attains a tight bound in the covariates dimension d, O(log d).
Thursday, April 26, 2018 - 3:00pm - 3:30pm
Maria-Pia Victoria-Feser (Universite de Geneve)
Along the ever increasing data size and model complexity, an important challenge frequently encountered in constructing new estimators or in implementing a classical one such as the maximum likelihood estimator, is the computational aspect of the estimation procedure. To carry out estimation, approximate methods such as pseudo-likelihood functions or approximated estimating equations are increasingly used in practice as these methods are typically easier to implement numerically although they can lead to inconsistent and/or biased estimators.
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