2 citations · 3 across the 6 of their papers we have counts for
7 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…
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…
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…
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…
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…
A Robust and Energy-Efficient Trajectory Planning Framework for High-Degree-of-Freedom Robots
Sajjad Hussain, Md Saad, Almas Baimagambetov +1
Energy efficiency and motion smoothness are essential in trajectory planning for high-degree-of-freedom robots to ensure optimal performance and reduce mechanical wear. This paper…