Machine Learning and Regularization

ESEM

Presenter(s)	Type			Length	Chair
Andrii Babii Jungjun Choi Yukun Ma Mario Martinoli	Contributed	25/08	09:00 UTC	90 mins	Anders Kock

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Papers

(Listed in order of presenters above)

Binary choice with asymmetric loss in a data-rich environment: theory and an application to racial justice

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Inference using Nuclear-norm Penalized Estimator and Its Applications

Dyadic Machine Learning: with an Application to High-Dimensional Dyadic-Robust Analysis of Determinants of Free Trade Agreements

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Nonparametric Moment-based Estimation of Simulated Models via Regularized Regression

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