5 papers
A clusterability test for directed graphs
Mario R. Guarracino, Pierre Miasnikof, Alexander Y. Shestopaloff +3
In this article, we extend a statistical test of graph clusterability, the test, to directed graphs with no self loops. The test, originally designed for undirected graphs,…
Data-to-Model Distillation: Data-Efficient Learning Framework
Ahmad Sajedi, Samir Khaki, Lucy Z. Liu +3
Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a mod…
Optimizing Deep Reinforcement Learning for American Put Option Hedging
Reilly Pickard, F. Wredenhagen, Y. Lawryshyn
This paper contributes to the existing literature on hedging American options with Deep Reinforcement Learning (DRL). The study first investigates hyperparameter impact on hedging…
Hedging American Put Options with Deep Reinforcement Learning
Reilly Pickard, Finn Wredenhagen, Julio DeJesus +2
This article leverages deep reinforcement learning (DRL) to hedge American put options, utilizing the deep deterministic policy gradient (DDPG) method. The agents are first trained…
Assisting humans in complex comparisons: automated information comparison at scale
Truman Yuen, Graham A. Watt, Yuri Lawryshyn
Generative Large Language Models enable efficient analytics across knowledge domains, rivalling human experts in information comparisons. However, the applications of LLMs for info…