6 papers
Online Domain-aware LLM Decoding for Continual Domain Evolution
Mohammad Abu-Shaira, Weishi Shi
LLMs are typically fine-tuned offline on domain-specific data, assuming a static domain. In practice, domain knowledge evolves continuously through new regulations, products, servi…
OLR-WA: Online Weighted Average Linear Regression in Multivariate Data Streams
Mohammad Abu-Shaira, Alejandro Rodriguez, Greg Speegle +2
Online learning updates models incrementally with new data, avoiding large storage requirements and costly model recalculations. In this paper, we introduce "OLR-WA; OnLine Regress…
Unveiling Statistical Significance of Online Regression over Multiple Datasets
Mohammad Abu-Shaira, Weishi Shi
Despite extensive focus on techniques for evaluating the performance of two learning algorithms on a single dataset, the critical challenge of developing statistical tests to compa…
OLC-WA: Drift Aware Tuning-Free Online Classification with Weighted Average
Mohammad Abu Shaira, Yunhe Feng, Heng Fan +1
Real-world data sets often exhibit temporal dynamics characterized by evolving data distributions. Disregarding this phenomenon, commonly referred to as concept drift, can signific…
OLR-WAA: Adaptive and Drift-Resilient Online Regression with Dynamic Weighted Averaging
Mohammad Abu-Shaira, Weishi Shi
Real-world datasets frequently exhibit evolving data distributions, reflecting temporal variations and underlying shifts. Overlooking this phenomenon, known as concept drift, can s…
DAO-GP Drift Aware Online Non-Linear Regression Gaussian-Process
Mohammad Abu-Shaira, Ajita Rattani, Weishi Shi
Real-world datasets often exhibit temporal dynamics characterized by evolving data distributions. Disregarding this phenomenon, commonly referred to as concept drift, can significa…