We consider the linear discriminant analysis problem in the high-dimensi...
We consider a class of assortment optimization problems in an offline
da...
Assortment optimization has received active explorations in the past few...
We propose a novel combinatorial inference framework to conduct general
...
Bregman proximal point algorithm (BPPA), as one of the centerpieces in t...
Strong correlations between explanatory variables are problematic for
hi...
While deep reinforcement learning has achieved tremendous successes in
v...
We consider the stochastic contextual bandit problem under the high
dime...
Adversarial training is a principled approach for training robust neural...
In statistical methods, interactions are the contributions from the prod...
Existing nonconvex statistical optimization theory and methods crucially...
This paper studies the matrix completion problem under arbitrary samplin...
Consider the stochastic composition optimization problem where the objec...
This paper proposes a decorrelation-based approach to test hypotheses an...
Classical stochastic gradient methods are well suited for minimizing
exp...