We consider a robust reinforcement learning problem, where a learning ag...
In this paper we consider the contextual multi-armed bandit problem for
...
Bayesian optimization (BO) is a sample-efficient approach for tuning des...
We investigate the role of noise in optimization algorithms for learning...
We consider selecting the top-m alternatives from a finite number of
alt...
Numerous empirical evidences have corroborated the importance of noise i...
Group testing pools multiple samples together and performs tests on thes...
In this paper, we aim to solve Bayesian Risk Optimization (BRO), which i...
We consider Bayesian optimization of objective functions of the form ρ[
...
Stochastic simulation has been widely used to analyze the performance of...
Residual Network (ResNet) is undoubtedly a milestone in deep learning. R...
Numerous empirical evidence has corroborated that the noise plays a cruc...
Asynchronous momentum stochastic gradient descent algorithms (Async-MSGD...
Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely ap...
Performing a computer experiment can be viewed as observing a mapping be...