159 citations · 194 across the 29 of their papers we have counts for
13 papers · 1 filter
Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics
Dhiraj Neupane, Mohamed Reda Bouadjenek, Richard Dazeley +1
Machinery fault detection (MFD) remains heavily reliant on supervised learning, which struggles with the scarcity of fault labels in real-world settings. While reinforcement learni…
Memory Merge DQN: Sensitivity Weighted Target Updates for Stable Value Learning
Adrian Ly, Richard Dazeley, Peter Vamplew +2
Deep Q-networks use target networks to stabilise bootstrapped value learning, but the standard hard copy update also introduces a tradeoff. Holding the target network fixed, improv…
Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
Dhiraj Neupane, Richard Dazeley, Mohamed Reda Bouadjenek +1
Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL's sequential deci…
IVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal%
Handling missing data remains a fundamental challenge in real-world tabular datasets, especially when data are heterogeneous with both numerical and categorical features. Existing…
MissHDD: Hybrid Deterministic Diffusion for Hetrogeneous Incomplete Data Imputation
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
Incomplete data are common in real-world tabular applications, where numerical, categorical, and discrete attributes coexist within a single dataset. This heterogeneous structure p…
MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
Incomplete data is a persistent challenge in real-world datasets, often governed by complex and unobservable missing mechanisms. Simulating missingness has become a standard approa…
Handling Out-of-Distribution Data: A Survey
Lakpa Tamang, Mohamed Reda Bouadjenek, Richard Dazeley +1
In the field of Machine Learning (ML) and data-driven applications, one of the significant challenge is the change in data distribution between the training and deployment stages,…
Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation
Aymen Rayane Khouas, Mohamed Reda Bouadjenek, Hakim Hacid +1
Graph federated recommendation systems offer a privacy-preserving alternative to traditional centralized recommendation architectures, which often raise concerns about data securit…
Rolling Ball Optimizer: Learning by ironing out loss landscape wrinkles
Mohammed Djameleddine Belgoumri, Mohamed Reda Bouadjenek, Hakim Hacid +2
Training large neural networks (NNs) requires optimizing high-dimensional data-dependent loss functions. The optimization landscape of these functions is often highly complex and t…
Developing robust methods to handle missing data in real-world applications effectively
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
Missing data is a pervasive challenge spanning diverse data types, including tabular, sensor data, time-series, images and so on. Its origins are multifaceted, resulting in various…
HI-PMK: A Data-Dependent Kernel for Incomplete Heterogeneous Data Representation
Youran Zhou, Mohamed Reda Bouadjenek, Jonathan Wells +1
Handling incomplete and heterogeneous data remains a central challenge in real-world machine learning, where missing values may follow complex mechanisms (MCAR, MAR, MNAR) and feat…
Data Quality in Edge Machine Learning: A State-of-the-Art Survey
Mohammed Djameleddine Belgoumri, Mohamed Reda Bouadjenek, Sunil Aryal +1
Data-driven Artificial Intelligence (AI) systems trained using Machine Learning (ML) are shaping an ever-increasing (in size and importance) portion of our lives, including, but no…
Training Machine Learning models at the Edge: A Survey
Aymen Rayane Khouas, Mohamed Reda Bouadjenek, Hakim Hacid +1
Edge computing has gained significant traction in recent years, promising enhanced efficiency by integrating artificial intelligence capabilities at the edge. While the focus has p…