6 papers
Statistical Arbitrage in Options Markets by Graph Learning and Synthetic Long Positions
Yoonsik Hong, Diego Klabjan
Statistical arbitrages (StatArbs) driven by machine learning has garnered considerable attention in both academia and industry. Nevertheless, deep-learning (DL) approaches to direc…
Graph Learning for Foreign Exchange Rate Prediction and Statistical Arbitrage
Yoonsik Hong, Diego Klabjan
We propose a two-step graph learning approach for foreign exchange statistical arbitrages (FXSAs), addressing two key gaps in prior studies: the absence of graph-learning methods f…
DiffMVR: Diffusion-based Automated Multi-Guidance Video Restoration
Zheyan Zhang, Diego Klabjan, Renee CB Manworren
In this work, we address a challenge in video inpainting: reconstructing occluded regions in dynamic, real-world scenarios. Motivated by the need for continuous human motion monito…
LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
Huiyu Wu, Diego Klabjan
Federated Learning (FL) is a collaborative, privacy-preserving machine learning framework that enables multiple participants to train a single global model. However, the recent adv…
Decentralized Blockchain-based Robust Multi-agent Multi-armed Bandit
Mengfan Xu, Diego Klabjan
We study a robust, i.e. in presence of malicious participants, multi-agent multi-armed bandit problem where multiple participants are distributed on a fully decentralized blockchai…
Robust softmax aggregation on blockchain based federated learning with convergence guarantee
Huiyu Wu, Diego Klabjan
Blockchain based federated learning is a distributed learning scheme that allows model training without participants sharing their local data sets, where the blockchain components…