Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-the-art performance ...

Abstract: Distributional approximation is a fundamental problem in machine learning with numerous applications across all fields of science and engineering and beyond. The key challenge in most ...

We propose two nonparametric tests for the null of no event-induced shifts in the location parameter of the distribution of cross-sectionally dependent stock returns and develop their distributional ...

Abstract: Because of the equilibrium between mathematical tractability and approximation accuracy maintained by the inverse Gaussian (IG) distributional model, it has been regarded as the most ...

This talk surveys three challenge areas for mechanism design and describes the role approximation plays in resolving them. Challenge 1: optimal mechanisms are parameterized by knowledge of the ...