8 papers
Cluster-Specific Localized Drift Detection for Efficient Batch Model Adaptation under Controlled Distribution Shift
Ignacio Cabrera Martin, Marcello Trovati, Almas Baimagambetov +1
Machine learning systems deployed in dynamic environments frequently operate under nonstationary data distributions, where controlled distribution shift can progressively degrade p…
Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Xuanyan Liu, Ignacio Cabrera Martin, Marcello Trovati +2
The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread availability of machine learning…
Evolving Machine Learning in Non-Stationary Environments: A Unified Survey of Drift, Forgetting, and Adaptation
Ignacio Cabrera Martin, Subhaditya Mukherjee, Almas Baimagambetov +2
In an era defined by rapid data evolution, traditional Machine Learning (ML) models often struggle to adapt to dynamic environments. Evolving Machine Learning (EML) has emerged as…
Long-Sequence LSTM Modeling for NBA Game Outcome Prediction Using a Novel Multi-Season Dataset
Charles Rios, Longzhen Han, Almas Baimagambetov +1
Predicting the outcomes of professional basketball games, particularly in the National Basketball Association (NBA), has become increasingly important for coaching strategy, fan en…
A Survey of Generative Categories and Techniques in Multimodal Generative Models
Longzhen Han, Awes Mubarak, Almas Baimagambetov +2
Multimodal Generative Models (MGMs) have rapidly evolved beyond text generation, now spanning diverse output modalities including images, music, video, human motion, and 3D objects…
Ethical AI prompt recommendations in large language models using collaborative filtering
Jordan Nelson, Almas Baimagambetov, Konstantinos Avgerinakis +1
As large language models (LLMs) shape AI development, ensuring ethical prompt recommendations is crucial. LLMs offer innovation but risk bias, fairness issues, and accountability c…