Projection Bias

Projection bias, in various contexts, refers to the systematic error in predicting future states or estimating unknown quantities based on current perspectives. Current research focuses on improving projection techniques across diverse fields, including dimensionality reduction (e.g., using UMAP, t-SNE, and novel algorithms like TopoMap++), Bayesian optimization (with methods like CEPBO), and efficient model training (employing low-rank projections and techniques like GaLore and OwLore). These advancements enhance data visualization, optimize complex processes, and enable memory-efficient training of large models, impacting fields from machine learning and computer vision to medical image analysis and causal inference.