Abstract: Decision-making in urban autonomous driving scenarios presents significant challenges due to the highly stochastic and interactive nature of traffic participants. While reinforcement ...

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 ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...

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 ...

Distributionalは、AIの信頼性向上を目指す最先端のAIテストプラットフォームを提供する企業です。生成AIの導入初期段階から、数千の機械学習（ML）モデルを本番環境で運用する大規模な組織、さらには複数の非定常コンポーネントを含む複雑なAI/ML ...

This paper examines the properties of various approximation methods for solving stochastic dynamic programs in structural estimation problems. The problem addressed is evaluating the expected value of ...