model with the EM algorithm, which is equivalent to an iteratively re-weighted Poisson-gamma model on an augmented data set …

model comparison. LightGBM algorithm outperforms the other 14 models acording to both RMSE and MAE criteria, and is the only …

setting, humans cannot verify the algorithm's quality. We, therefore, argue that algorithms exhibit traits of a credence good … intervention, we explain the way the algorithm functions. We find that while explanation helps participants recognize bias in the … algorithm, it remarkably decreases human decision-making performance. In the second treatment, we reveal the task's correct …

and one of them is Grasshopper Optimization Algorithm (GOA). Despite the fact that some binary versions of the GOA have …, effectively. The performance of the proposed algorithm is investigated on Cap and M* series datasets and the obtained results are …

a more particular need for algorithm-centered policy responses. Specifically, algorithmic transparency … algorithmic “black box” and “interpret” the algorithm’s decision-making process should play a key role. In appropriate cases …

We propose an actor-critic reinforcement learning (RL) algorithm for the optimal execution problem. We consider the …. We develop a finite-time error analysis of our algorithm and show that it converges linearly under suitable conditions on … the learning rates. We test our algorithm in three different types of market simulators built on the Almgren-Chriss model …