7 citations · 10 across the 11 of their papers we have counts for
11 papers
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
Jaeyeon Jang, Diego Klabjan, Veena Mendiratta +1
Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server. Most exis…
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…
Semi-supervised 3D Video Information Retrieval with Deep Neural Network and Bi-directional Dynamic-time Warping Algorithm
Yintai Ma, Diego Klabjan
This paper presents a novel semi-supervised deep learning algorithm for retrieving similar 2D and 3D videos based on visual content. The proposed approach combines the power of dee…
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading
Shuyang Wang, Diego Klabjan
We propose an ensemble method to improve the generalization performance of trading strategies trained by deep reinforcement learning algorithms in a highly stochastic environment o…
Regret Lower Bounds in Multi-agent Multi-armed Bandit
Mengfan Xu, Diego Klabjan
Multi-armed Bandit motivates methods with provable upper bounds on regret and also the counterpart lower bounds have been extensively studied in this context. Recently, Multi-agent…
Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints
Jaeyeon Jang, Diego Klabjan, Han Liu +5
This paper proposes a novel multi-agent reinforcement learning (MARL) method to learn multiple coordinated agents under directed acyclic graph (DAG) constraints. Unlike existing MA…