Uncertainty quantification for prediction is an intriguing problem with
...
Deep neural networks have achieved tremendous success due to their
repre...
Time-varying stochastic optimization problems frequently arise in machin...
Many instances of algorithmic bias are caused by distributional shifts. ...
Post-processing in algorithmic fairness is a versatile approach for
corr...
This paper presents a number of new findings about the canonical change ...
Regression discontinuity design models are widely used for the assessmen...
Optimal transport (OT) provides a way of measuring distances between
dis...
One of the main barriers to the broader adoption of algorithmic fairness...
We study and predict the evolution of Covid-19 in six US states from the...
Individual fairness is an intuitive definition of algorithmic fairness t...
Linear thresholding models postulate that the conditional distribution o...
Manski's celebrated maximum score estimator for the binary choice model ...