Given a sequence of observable variables {(x_1, y_1), …, (x_n,
y_n)}, th...
Optimal transport theory has provided machine learning with several tool...
It is common in machine learning to estimate a response y given covariat...
In predictive modeling for high-stake decision-making, predictors must b...
When one observes a sequence of variables (x_1, y_1), ..., (x_n, y_n),
c...
Path-following algorithms are frequently used in composite optimization
...
Conformal prediction constructs a confidence set for an unobserved respo...
Screening rules were recently introduced as a technique for explicitly
i...
If you are predicting the label y of a new object with ŷ, how
confident ...
Popular machine learning estimators involve regularization parameters th...
In high dimensional regression settings, sparsity enforcing penalties ha...
In high dimensional settings, sparse structures are crucial for efficien...
In high dimensional settings, sparse structures are crucial for efficien...
High dimensional regression benefits from sparsity promoting regularizat...