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Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
This paper proposes SAGD-IV, a novel framework for conducting nonparametric instrumental variable (NPIV) regression by employing stochastic approximate gradients to minimize the projected populational risk.
Contextual Inverse Optimization: Offline and Online Learning
In the online setting, we leverage this geometric characterization to optimize the cumulative regret.
BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions
In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees.
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